id	sid	tid	token	lemma	pos
fcis-22002	1	1	frontiers	frontier	NOUN
fcis-22002	1	2	in	in	ADP
fcis-22002	1	3	computing	computing	NOUN
fcis-22002	1	4	and	and	CCONJ
fcis-22002	1	5	intelligent	intelligent	ADJ
fcis-22002	1	6	systems	system	NOUN
fcis-22002	1	7	issn	issn	VERB
fcis-22002	1	8	:	:	PUNCT
fcis-22002	1	9	2832	2832	NUM
fcis-22002	1	10	-	-	SYM
fcis-22002	1	11	6024	6024	NUM
fcis-22002	1	12	|	|	NOUN
fcis-22002	1	13	vol	vol	NOUN
fcis-22002	1	14	.	.	PROPN
fcis-22002	2	1	8	8	NUM
fcis-22002	2	2	,	,	PUNCT
fcis-22002	2	3	no	no	INTJ
fcis-22002	2	4	.	.	NOUN
fcis-22002	2	5	2	2	NUM
fcis-22002	2	6	,	,	PUNCT
fcis-22002	2	7	2024	2024	NUM
fcis-22002	2	8	22	22	NUM
fcis-22002	2	9	end‐to‐end	end‐to‐end	NOUN
fcis-22002	2	10	speech	speech	NOUN
fcis-22002	2	11	hash	hash	NOUN
fcis-22002	2	12	retrieval	retrieval	NOUN
fcis-22002	2	13	algorithm	algorithm	NOUN
fcis-22002	2	14	based	base	VERB
fcis-22002	2	15	on	on	ADP
fcis-22002	2	16	speech	speech	NOUN
fcis-22002	2	17	content	content	NOUN
fcis-22002	2	18	and	and	CCONJ
fcis-22002	2	19	pre‐training	pre‐traine	VERB
fcis-22002	2	20	yian	yian	ADJ
fcis-22002	2	21	li	li	PROPN
fcis-22002	2	22	*	*	PROPN
fcis-22002	2	23	,	,	PUNCT
fcis-22002	2	24	yibo	yibo	PROPN
fcis-22002	2	25	huang	huang	PROPN
fcis-22002	2	26	college	college	PROPN
fcis-22002	2	27	of	of	ADP
fcis-22002	2	28	physics	physics	PROPN
fcis-22002	2	29	and	and	CCONJ
fcis-22002	2	30	electronic	electronic	ADJ
fcis-22002	2	31	engineering	engineering	NOUN
fcis-22002	2	32	,	,	PUNCT
fcis-22002	2	33	northwest	northwest	PROPN
fcis-22002	2	34	normal	normal	ADJ
fcis-22002	2	35	university	university	NOUN
fcis-22002	2	36	,	,	PUNCT
fcis-22002	2	37	lanzhou	lanzhou	PROPN
fcis-22002	2	38	gansu	gansu	PROPN
fcis-22002	2	39	,	,	PUNCT
fcis-22002	2	40	730070	730070	NUM
fcis-22002	2	41	,	,	PUNCT
fcis-22002	2	42	china	china	PROPN
fcis-22002	2	43	*	*	PUNCT
fcis-22002	2	44	corresponding	correspond	VERB
fcis-22002	2	45	author	author	NOUN
fcis-22002	2	46	:	:	PUNCT
fcis-22002	2	47	yian	yian	PROPN
fcis-22002	2	48	li	li	PROPN
fcis-22002	2	49	(	(	PUNCT
fcis-22002	2	50	email	email	NOUN
fcis-22002	2	51	:	:	PUNCT
fcis-22002	2	52	15161122091@163.com	15161122091@163.com	NUM
fcis-22002	2	53	)	)	PUNCT
fcis-22002	2	54	abstract	abstract	NOUN
fcis-22002	2	55	:	:	PUNCT
fcis-22002	2	56	traditional	traditional	ADJ
fcis-22002	2	57	speech	speech	NOUN
fcis-22002	2	58	retrieval	retrieval	NOUN
fcis-22002	2	59	tasks	task	NOUN
fcis-22002	2	60	,	,	PUNCT
fcis-22002	2	61	such	such	ADJ
fcis-22002	2	62	as	as	ADP
fcis-22002	2	63	audio	audio	ADJ
fcis-22002	2	64	fingerprinting	fingerprinting	NOUN
fcis-22002	2	65	and	and	CCONJ
fcis-22002	2	66	spoken	spoken	ADJ
fcis-22002	2	67	word	word	NOUN
fcis-22002	2	68	detection	detection	NOUN
fcis-22002	2	69	query	query	NOUN
fcis-22002	2	70	(	(	PUNCT
fcis-22002	2	71	std	std	NOUN
fcis-22002	2	72	-	-	NOUN
fcis-22002	2	73	qbe	qbe	NOUN
fcis-22002	2	74	)	)	PUNCT
fcis-22002	2	75	,	,	PUNCT
fcis-22002	2	76	focus	focus	VERB
fcis-22002	2	77	on	on	ADP
fcis-22002	2	78	feature	feature	NOUN
fcis-22002	2	79	matching	match	VERB
fcis-22002	2	80	for	for	ADP
fcis-22002	2	81	speech	speech	NOUN
fcis-22002	2	82	or	or	CCONJ
fcis-22002	2	83	keyword	keyword	NOUN
fcis-22002	2	84	retrieval	retrieval	NOUN
fcis-22002	2	85	.	.	PUNCT
fcis-22002	3	1	in	in	ADP
fcis-22002	3	2	this	this	DET
fcis-22002	3	3	paper	paper	NOUN
fcis-22002	3	4	,	,	PUNCT
fcis-22002	3	5	we	we	PRON
fcis-22002	3	6	present	present	VERB
fcis-22002	3	7	a	a	DET
fcis-22002	3	8	content	content	NOUN
fcis-22002	3	9	-	-	PUNCT
fcis-22002	3	10	based	base	VERB
fcis-22002	3	11	speech	speech	NOUN
fcis-22002	3	12	retrieval	retrieval	NOUN
fcis-22002	3	13	algorithm	algorithm	NOUN
fcis-22002	3	14	.	.	PUNCT
fcis-22002	4	1	the	the	DET
fcis-22002	4	2	algorithm	algorithm	NOUN
fcis-22002	4	3	allows	allow	VERB
fcis-22002	4	4	matching	match	VERB
fcis-22002	4	5	based	base	VERB
fcis-22002	4	6	on	on	ADP
fcis-22002	4	7	the	the	DET
fcis-22002	4	8	complete	complete	ADJ
fcis-22002	4	9	content	content	NOUN
fcis-22002	4	10	of	of	ADP
fcis-22002	4	11	sentences	sentence	NOUN
fcis-22002	4	12	in	in	ADP
fcis-22002	4	13	speech	speech	NOUN
fcis-22002	4	14	,	,	PUNCT
fcis-22002	4	15	not	not	PART
fcis-22002	4	16	just	just	ADV
fcis-22002	4	17	local	local	ADJ
fcis-22002	4	18	features	feature	NOUN
fcis-22002	4	19	or	or	CCONJ
fcis-22002	4	20	keywords	keyword	NOUN
fcis-22002	4	21	.	.	PUNCT
fcis-22002	5	1	importantly	importantly	ADV
fcis-22002	5	2	,	,	PUNCT
fcis-22002	5	3	it	it	PRON
fcis-22002	5	4	completely	completely	ADV
fcis-22002	5	5	bypasses	bypass	VERB
fcis-22002	5	6	the	the	DET
fcis-22002	5	7	automatic	automatic	ADJ
fcis-22002	5	8	speech	speech	NOUN
fcis-22002	5	9	recognition	recognition	NOUN
fcis-22002	5	10	(	(	PUNCT
fcis-22002	5	11	asr	asr	NOUN
fcis-22002	5	12	)	)	PUNCT
fcis-22002	5	13	transcription	transcription	NOUN
fcis-22002	5	14	process	process	NOUN
fcis-22002	5	15	by	by	ADP
fcis-22002	5	16	mapping	map	VERB
fcis-22002	5	17	the	the	DET
fcis-22002	5	18	acoustic	acoustic	ADJ
fcis-22002	5	19	features	feature	NOUN
fcis-22002	5	20	of	of	ADP
fcis-22002	5	21	the	the	DET
fcis-22002	5	22	sentence	sentence	NOUN
fcis-22002	5	23	directly	directly	ADV
fcis-22002	5	24	to	to	ADP
fcis-22002	5	25	the	the	DET
fcis-22002	5	26	hamming	hamming	NOUN
fcis-22002	5	27	space	space	NOUN
fcis-22002	5	28	.	.	PUNCT
fcis-22002	6	1	retrieval	retrieval	NOUN
fcis-22002	6	2	of	of	ADP
fcis-22002	6	3	the	the	DET
fcis-22002	6	4	same	same	ADJ
fcis-22002	6	5	content	content	NOUN
fcis-22002	6	6	is	be	AUX
fcis-22002	6	7	then	then	ADV
fcis-22002	6	8	achieved	achieve	VERB
fcis-22002	6	9	by	by	ADP
fcis-22002	6	10	comparing	compare	VERB
fcis-22002	6	11	hamming	hamming	ADJ
fcis-22002	6	12	distances	distance	NOUN
fcis-22002	6	13	,	,	PUNCT
fcis-22002	6	14	thus	thus	ADV
fcis-22002	6	15	effectively	effectively	ADV
fcis-22002	6	16	eliminating	eliminate	VERB
fcis-22002	6	17	the	the	DET
fcis-22002	6	18	potential	potential	ADJ
fcis-22002	6	19	impact	impact	NOUN
fcis-22002	6	20	of	of	ADP
fcis-22002	6	21	transcription	transcription	NOUN
fcis-22002	6	22	errors	error	NOUN
fcis-22002	6	23	on	on	ADP
fcis-22002	6	24	retrieval	retrieval	NOUN
fcis-22002	6	25	performance	performance	NOUN
fcis-22002	6	26	.	.	PUNCT
fcis-22002	7	1	in	in	ADP
fcis-22002	7	2	order	order	NOUN
fcis-22002	7	3	to	to	PART
fcis-22002	7	4	achieve	achieve	VERB
fcis-22002	7	5	this	this	PRON
fcis-22002	7	6	,	,	PUNCT
fcis-22002	7	7	our	our	PRON
fcis-22002	7	8	approach	approach	NOUN
fcis-22002	7	9	employs	employ	VERB
fcis-22002	7	10	the	the	DET
fcis-22002	7	11	connectionist	connectionist	ADJ
fcis-22002	7	12	temporal	temporal	ADJ
fcis-22002	7	13	classification	classification	NOUN
fcis-22002	7	14	(	(	PUNCT
fcis-22002	7	15	ctc	ctc	NOUN
fcis-22002	7	16	)	)	PUNCT
fcis-22002	7	17	speech	speech	NOUN
fcis-22002	7	18	recognition	recognition	NOUN
fcis-22002	7	19	technique	technique	NOUN
fcis-22002	7	20	to	to	AUX
fcis-22002	7	21	pre	pre	VERB
fcis-22002	7	22	-	-	VERB
fcis-22002	7	23	train	train	VERB
fcis-22002	7	24	the	the	DET
fcis-22002	7	25	model	model	NOUN
fcis-22002	7	26	to	to	PART
fcis-22002	7	27	learn	learn	VERB
fcis-22002	7	28	content	content	NOUN
fcis-22002	7	29	-	-	PUNCT
fcis-22002	7	30	dependent	dependent	ADJ
fcis-22002	7	31	representations	representation	NOUN
fcis-22002	7	32	of	of	ADP
fcis-22002	7	33	speech	speech	NOUN
fcis-22002	7	34	features	feature	NOUN
fcis-22002	7	35	.	.	PUNCT
fcis-22002	8	1	through	through	ADP
fcis-22002	8	2	experiments	experiment	NOUN
fcis-22002	8	3	,	,	PUNCT
fcis-22002	8	4	we	we	PRON
fcis-22002	8	5	demonstrate	demonstrate	VERB
fcis-22002	8	6	that	that	SCONJ
fcis-22002	8	7	our	our	PRON
fcis-22002	8	8	approach	approach	NOUN
fcis-22002	8	9	achieves	achieve	VERB
fcis-22002	8	10	excellent	excellent	ADJ
fcis-22002	8	11	performance	performance	NOUN
fcis-22002	8	12	in	in	ADP
fcis-22002	8	13	speech	speech	NOUN
fcis-22002	8	14	retrieval	retrieval	NOUN
fcis-22002	8	15	tasks	task	NOUN
fcis-22002	8	16	.	.	PUNCT
fcis-22002	9	1	keywords	keyword	NOUN
fcis-22002	9	2	:	:	PUNCT
fcis-22002	9	3	speech	speech	NOUN
fcis-22002	9	4	retrieval	retrieval	NOUN
fcis-22002	9	5	;	;	PUNCT
fcis-22002	9	6	deep	deep	ADJ
fcis-22002	9	7	hashing	hashing	NOUN
fcis-22002	9	8	;	;	PUNCT
fcis-22002	9	9	wavenet	wavenet	NOUN
fcis-22002	9	10	;	;	PUNCT
fcis-22002	9	11	transformer	transformer	NOUN
fcis-22002	9	12	;	;	PUNCT
fcis-22002	9	13	pre	pre	ADJ
fcis-22002	9	14	-	-	NOUN
fcis-22002	9	15	training	training	NOUN
fcis-22002	9	16	.	.	PUNCT
fcis-22002	10	1	1	1	X
fcis-22002	10	2	.	.	X
fcis-22002	10	3	introduction	introduction	NOUN
fcis-22002	10	4	speech	speech	NOUN
fcis-22002	10	5	is	be	AUX
fcis-22002	10	6	gaining	gain	VERB
fcis-22002	10	7	prominence	prominence	NOUN
fcis-22002	10	8	in	in	ADP
fcis-22002	10	9	today	today	NOUN
fcis-22002	10	10	's	's	PART
fcis-22002	10	11	society	society	NOUN
fcis-22002	10	12	as	as	ADP
fcis-22002	10	13	one	one	NUM
fcis-22002	10	14	of	of	ADP
fcis-22002	10	15	the	the	DET
fcis-22002	10	16	most	most	ADV
fcis-22002	10	17	relied	rely	VERB
fcis-22002	10	18	upon	upon	SCONJ
fcis-22002	10	19	forms	form	NOUN
fcis-22002	10	20	of	of	ADP
fcis-22002	10	21	expression	expression	NOUN
fcis-22002	10	22	in	in	ADP
fcis-22002	10	23	modern	modern	ADJ
fcis-22002	10	24	times	time	NOUN
fcis-22002	10	25	.	.	PUNCT
fcis-22002	11	1	written	write	VERB
fcis-22002	11	2	modes	mode	NOUN
fcis-22002	11	3	of	of	ADP
fcis-22002	11	4	communication	communication	NOUN
fcis-22002	11	5	,	,	PUNCT
fcis-22002	11	6	such	such	ADJ
fcis-22002	11	7	as	as	ADP
fcis-22002	11	8	electronic	electronic	ADJ
fcis-22002	11	9	and	and	CCONJ
fcis-22002	11	10	text	text	NOUN
fcis-22002	11	11	messages	message	NOUN
fcis-22002	11	12	,	,	PUNCT
fcis-22002	11	13	may	may	AUX
fcis-22002	11	14	not	not	PART
fcis-22002	11	15	convey	convey	VERB
fcis-22002	11	16	information	information	NOUN
fcis-22002	11	17	with	with	ADP
fcis-22002	11	18	complete	complete	ADJ
fcis-22002	11	19	accuracy	accuracy	NOUN
fcis-22002	11	20	because	because	SCONJ
fcis-22002	11	21	humans	human	NOUN
fcis-22002	11	22	may	may	AUX
fcis-22002	11	23	misunderstand	misunderstand	VERB
fcis-22002	11	24	them	they	PRON
fcis-22002	11	25	.	.	PUNCT
fcis-22002	12	1	textual	textual	ADJ
fcis-22002	12	2	modes	mode	NOUN
fcis-22002	12	3	of	of	ADP
fcis-22002	12	4	communication	communication	NOUN
fcis-22002	12	5	may	may	AUX
fcis-22002	12	6	not	not	PART
fcis-22002	12	7	be	be	AUX
fcis-22002	12	8	able	able	ADJ
fcis-22002	12	9	to	to	PART
fcis-22002	12	10	convey	convey	VERB
fcis-22002	12	11	information	information	NOUN
fcis-22002	12	12	with	with	ADP
fcis-22002	12	13	complete	complete	ADJ
fcis-22002	12	14	accuracy	accuracy	NOUN
fcis-22002	12	15	because	because	SCONJ
fcis-22002	12	16	humans	human	NOUN
fcis-22002	12	17	may	may	AUX
fcis-22002	12	18	misunderstand	misunderstand	VERB
fcis-22002	12	19	them	they	PRON
fcis-22002	12	20	.	.	PUNCT
fcis-22002	13	1	speech	speech	NOUN
fcis-22002	13	2	,	,	PUNCT
fcis-22002	13	3	on	on	ADP
fcis-22002	13	4	the	the	DET
fcis-22002	13	5	other	other	ADJ
fcis-22002	13	6	hand	hand	NOUN
fcis-22002	13	7	,	,	PUNCT
fcis-22002	13	8	does	do	AUX
fcis-22002	13	9	not	not	PART
fcis-22002	13	10	have	have	VERB
fcis-22002	13	11	this	this	DET
fcis-22002	13	12	problem	problem	NOUN
fcis-22002	14	1	[	[	X
fcis-22002	14	2	1][2	1][2	NUM
fcis-22002	14	3	]	]	PUNCT
fcis-22002	14	4	.	.	PUNCT
fcis-22002	15	1	in	in	ADP
fcis-22002	15	2	addition	addition	NOUN
fcis-22002	15	3	,	,	PUNCT
fcis-22002	15	4	with	with	ADP
fcis-22002	15	5	addition	addition	NOUN
fcis-22002	15	6	,	,	PUNCT
fcis-22002	15	7	voice	voice	NOUN
fcis-22002	15	8	interaction	interaction	NOUN
fcis-22002	15	9	is	be	AUX
fcis-22002	15	10	becoming	become	VERB
fcis-22002	15	11	more	more	ADV
fcis-22002	15	12	and	and	CCONJ
fcis-22002	15	13	more	more	ADV
fcis-22002	15	14	common	common	ADJ
fcis-22002	15	15	with	with	ADP
fcis-22002	15	16	the	the	DET
fcis-22002	15	17	popularity	popularity	NOUN
fcis-22002	15	18	of	of	ADP
fcis-22002	15	19	mobile	mobile	ADJ
fcis-22002	15	20	and	and	CCONJ
fcis-22002	15	21	smart	smart	ADJ
fcis-22002	15	22	home	home	NOUN
fcis-22002	15	23	devices	device	NOUN
fcis-22002	15	24	,	,	PUNCT
fcis-22002	15	25	making	make	VERB
fcis-22002	15	26	speech	speech	NOUN
fcis-22002	15	27	technology	technology	NOUN
fcis-22002	15	28	more	more	ADJ
fcis-22002	15	29	and	and	CCONJ
fcis-22002	15	30	more	more	ADV
fcis-22002	15	31	feasible	feasible	ADJ
fcis-22002	15	32	.	.	PUNCT
fcis-22002	16	1	content	content	NOUN
fcis-22002	16	2	-	-	PUNCT
fcis-22002	16	3	based	base	VERB
fcis-22002	16	4	speech	speech	NOUN
fcis-22002	16	5	retrieval	retrieval	NOUN
fcis-22002	16	6	allows	allow	VERB
fcis-22002	16	7	users	user	NOUN
fcis-22002	16	8	to	to	PART
fcis-22002	16	9	search	search	VERB
fcis-22002	16	10	for	for	ADP
fcis-22002	16	11	similar	similar	ADJ
fcis-22002	16	12	or	or	CCONJ
fcis-22002	16	13	matching	matching	NOUN
fcis-22002	16	14	content	content	NOUN
fcis-22002	16	15	directly	directly	ADV
fcis-22002	16	16	in	in	ADP
fcis-22002	16	17	large	large	ADJ
fcis-22002	16	18	speech	speech	NOUN
fcis-22002	16	19	databases	database	NOUN
fcis-22002	16	20	using	use	VERB
fcis-22002	16	21	speech	speech	NOUN
fcis-22002	16	22	segments	segment	NOUN
fcis-22002	16	23	as	as	ADP
fcis-22002	16	24	query	query	NOUN
fcis-22002	16	25	criteria	criterion	NOUN
fcis-22002	16	26	.	.	PUNCT
fcis-22002	17	1	the	the	DET
fcis-22002	17	2	advantage	advantage	NOUN
fcis-22002	17	3	of	of	ADP
fcis-22002	17	4	this	this	DET
fcis-22002	17	5	approach	approach	NOUN
fcis-22002	17	6	is	be	AUX
fcis-22002	17	7	that	that	SCONJ
fcis-22002	17	8	the	the	DET
fcis-22002	17	9	user	user	NOUN
fcis-22002	17	10	does	do	AUX
fcis-22002	17	11	not	not	PART
fcis-22002	17	12	need	need	VERB
fcis-22002	17	13	to	to	PART
fcis-22002	17	14	know	know	VERB
fcis-22002	17	15	the	the	DET
fcis-22002	17	16	exact	exact	ADJ
fcis-22002	17	17	user	user	NOUN
fcis-22002	17	18	only	only	ADV
fcis-22002	17	19	needs	need	VERB
fcis-22002	17	20	to	to	PART
fcis-22002	17	21	provide	provide	VERB
fcis-22002	17	22	a	a	DET
fcis-22002	17	23	speech	speech	NOUN
fcis-22002	17	24	sample	sample	NOUN
fcis-22002	17	25	to	to	PART
fcis-22002	17	26	complete	complete	VERB
fcis-22002	17	27	the	the	DET
fcis-22002	17	28	search	search	NOUN
fcis-22002	17	29	.	.	PUNCT
fcis-22002	18	1	retrieval	retrieval	NOUN
fcis-22002	18	2	.	.	PUNCT
fcis-22002	19	1	this	this	DET
fcis-22002	19	2	technique	technique	NOUN
fcis-22002	19	3	is	be	AUX
fcis-22002	19	4	particularly	particularly	ADV
fcis-22002	19	5	suitable	suitable	ADJ
fcis-22002	19	6	for	for	ADP
fcis-22002	19	7	speech	speech	NOUN
fcis-22002	19	8	-	-	PUNCT
fcis-22002	19	9	independent	independent	ADJ
fcis-22002	19	10	scenarios	scenario	NOUN
fcis-22002	19	11	such	such	ADJ
fcis-22002	19	12	as	as	ADP
fcis-22002	19	13	human	human	ADJ
fcis-22002	19	14	-	-	PUNCT
fcis-22002	19	15	machine	machine	NOUN
fcis-22002	19	16	speech	speech	NOUN
fcis-22002	19	17	interaction	interaction	NOUN
fcis-22002	19	18	.	.	PUNCT
fcis-22002	20	1	textindependent	textindependent	NOUN
fcis-22002	20	2	scenarios	scenario	NOUN
fcis-22002	20	3	such	such	ADJ
fcis-22002	20	4	as	as	ADP
fcis-22002	20	5	machine	machine	NOUN
fcis-22002	20	6	-	-	PUNCT
fcis-22002	20	7	speech	speech	NOUN
fcis-22002	20	8	interaction	interaction	NOUN
fcis-22002	20	9	.	.	PUNCT
fcis-22002	21	1	although	although	SCONJ
fcis-22002	21	2	some	some	DET
fcis-22002	21	3	progress	progress	NOUN
fcis-22002	21	4	has	have	AUX
fcis-22002	21	5	been	be	AUX
fcis-22002	21	6	made	make	VERB
fcis-22002	21	7	in	in	ADP
fcis-22002	21	8	speech	speech	NOUN
fcis-22002	21	9	retrieval	retrieval	NOUN
fcis-22002	21	10	technology	technology	NOUN
fcis-22002	21	11	,	,	PUNCT
fcis-22002	21	12	there	there	PRON
fcis-22002	21	13	are	be	VERB
fcis-22002	21	14	still	still	ADV
fcis-22002	21	15	some	some	DET
fcis-22002	21	16	unsolved	unsolved	ADJ
fcis-22002	21	17	problems	problem	NOUN
fcis-22002	21	18	and	and	CCONJ
fcis-22002	21	19	challenges	challenge	NOUN
fcis-22002	21	20	in	in	ADP
fcis-22002	21	21	practical	practical	ADJ
fcis-22002	21	22	applications	application	NOUN
fcis-22002	21	23	.	.	PUNCT
fcis-22002	22	1	mainstream	mainstream	NOUN
fcis-22002	22	2	content	content	NOUN
fcis-22002	22	3	-	-	PUNCT
fcis-22002	22	4	based	base	VERB
fcis-22002	22	5	speech	speech	NOUN
fcis-22002	22	6	retrieval	retrieval	NOUN
fcis-22002	22	7	algorithms	algorithm	NOUN
fcis-22002	22	8	generally	generally	ADV
fcis-22002	22	9	rely	rely	VERB
fcis-22002	22	10	on	on	ADP
fcis-22002	22	11	speech	speech	NOUN
fcis-22002	22	12	recognition	recognition	NOUN
fcis-22002	22	13	technology	technology	NOUN
fcis-22002	22	14	.	.	PUNCT
fcis-22002	23	1	this	this	DET
fcis-22002	23	2	technology	technology	NOUN
fcis-22002	23	3	firstly	firstly	ADV
fcis-22002	23	4	needs	need	VERB
fcis-22002	23	5	to	to	PART
fcis-22002	23	6	convert	convert	VERB
fcis-22002	23	7	speech	speech	NOUN
fcis-22002	23	8	content	content	NOUN
fcis-22002	23	9	into	into	ADP
fcis-22002	23	10	text	text	NOUN
fcis-22002	23	11	form	form	NOUN
fcis-22002	23	12	,	,	PUNCT
fcis-22002	23	13	and	and	CCONJ
fcis-22002	23	14	then	then	ADV
fcis-22002	23	15	utilize	utilize	VERB
fcis-22002	23	16	mature	mature	ADJ
fcis-22002	23	17	text	text	NOUN
fcis-22002	23	18	retrieval	retrieval	NOUN
fcis-22002	23	19	techniques	technique	NOUN
fcis-22002	23	20	to	to	PART
fcis-22002	23	21	realize	realize	VERB
fcis-22002	23	22	effective	effective	ADJ
fcis-22002	23	23	retrieval	retrieval	NOUN
fcis-22002	23	24	of	of	ADP
fcis-22002	23	25	information	information	NOUN
fcis-22002	23	26	.	.	PUNCT
fcis-22002	24	1	[	[	X
fcis-22002	24	2	3	3	NUM
fcis-22002	24	3	-	-	SYM
fcis-22002	24	4	6	6	NUM
fcis-22002	24	5	]	]	PUNCT
fcis-22002	24	6	this	this	DET
fcis-22002	24	7	approach	approach	NOUN
fcis-22002	24	8	not	not	PART
fcis-22002	24	9	only	only	ADV
fcis-22002	24	10	allows	allow	VERB
fcis-22002	24	11	for	for	ADP
fcis-22002	24	12	precise	precise	ADJ
fcis-22002	24	13	search	search	NOUN
fcis-22002	24	14	based	base	VERB
fcis-22002	24	15	on	on	ADP
fcis-22002	24	16	the	the	DET
fcis-22002	24	17	specific	specific	ADJ
fcis-22002	24	18	content	content	NOUN
fcis-22002	24	19	of	of	ADP
fcis-22002	24	20	speech	speech	NOUN
fcis-22002	24	21	,	,	PUNCT
fcis-22002	24	22	but	but	CCONJ
fcis-22002	24	23	also	also	ADV
fcis-22002	24	24	allows	allow	VERB
fcis-22002	24	25	for	for	ADP
fcis-22002	24	26	deeper	deep	ADJ
fcis-22002	24	27	exploration	exploration	NOUN
fcis-22002	24	28	of	of	ADP
fcis-22002	24	29	its	its	PRON
fcis-22002	24	30	underlying	underlying	ADJ
fcis-22002	24	31	semantic	semantic	ADJ
fcis-22002	24	32	information	information	NOUN
fcis-22002	24	33	.	.	PUNCT
fcis-22002	25	1	although	although	SCONJ
fcis-22002	25	2	the	the	DET
fcis-22002	25	3	use	use	NOUN
fcis-22002	25	4	of	of	ADP
fcis-22002	25	5	transcription	transcription	NOUN
fcis-22002	25	6	technology	technology	NOUN
fcis-22002	25	7	has	have	AUX
fcis-22002	25	8	largely	largely	ADV
fcis-22002	25	9	improved	improve	VERB
fcis-22002	25	10	the	the	DET
fcis-22002	25	11	accuracy	accuracy	NOUN
fcis-22002	25	12	of	of	ADP
fcis-22002	25	13	speech	speech	NOUN
fcis-22002	25	14	retrieval	retrieval	NOUN
fcis-22002	25	15	,	,	PUNCT
fcis-22002	25	16	its	its	PRON
fcis-22002	25	17	efficiency	efficiency	NOUN
fcis-22002	25	18	and	and	CCONJ
fcis-22002	25	19	accuracy	accuracy	NOUN
fcis-22002	25	20	are	be	AUX
fcis-22002	25	21	highly	highly	ADV
fcis-22002	25	22	dependent	dependent	ADJ
fcis-22002	25	23	on	on	ADP
fcis-22002	25	24	advanced	advanced	ADJ
fcis-22002	25	25	speech	speech	NOUN
fcis-22002	25	26	recognition	recognition	NOUN
fcis-22002	25	27	technology	technology	NOUN
fcis-22002	25	28	.	.	PUNCT
fcis-22002	26	1	building	build	VERB
fcis-22002	26	2	reliable	reliable	ADJ
fcis-22002	26	3	speech	speech	NOUN
fcis-22002	26	4	recognition	recognition	NOUN
fcis-22002	26	5	systems	system	NOUN
fcis-22002	26	6	requires	require	VERB
fcis-22002	26	7	large	large	ADJ
fcis-22002	26	8	transcription	transcription	NOUN
fcis-22002	26	9	datasets	dataset	NOUN
fcis-22002	26	10	and	and	CCONJ
fcis-22002	26	11	language	language	NOUN
fcis-22002	26	12	-	-	PUNCT
fcis-22002	26	13	specific	specific	ADJ
fcis-22002	26	14	expertise	expertise	NOUN
fcis-22002	26	15	.	.	PUNCT
fcis-22002	27	1	however	however	ADV
fcis-22002	27	2	,	,	PUNCT
fcis-22002	27	3	collecting	collect	VERB
fcis-22002	27	4	such	such	DET
fcis-22002	27	5	a	a	DET
fcis-22002	27	6	large	large	ADJ
fcis-22002	27	7	and	and	CCONJ
fcis-22002	27	8	accurate	accurate	ADJ
fcis-22002	27	9	dataset	dataset	NOUN
fcis-22002	27	10	is	be	AUX
fcis-22002	27	11	a	a	DET
fcis-22002	27	12	daunting	daunting	ADJ
fcis-22002	27	13	task	task	NOUN
fcis-22002	27	14	for	for	ADP
fcis-22002	27	15	many	many	ADJ
fcis-22002	27	16	application	application	NOUN
fcis-22002	27	17	scenarios	scenario	NOUN
fcis-22002	27	18	.	.	PUNCT
fcis-22002	28	1	in	in	ADP
fcis-22002	28	2	the	the	DET
fcis-22002	28	3	absence	absence	NOUN
fcis-22002	28	4	of	of	ADP
fcis-22002	28	5	sufficiently	sufficiently	ADV
fcis-22002	28	6	labeled	label	VERB
fcis-22002	28	7	data	datum	NOUN
fcis-22002	28	8	,	,	PUNCT
fcis-22002	28	9	speech	speech	NOUN
fcis-22002	28	10	recognition	recognition	NOUN
fcis-22002	28	11	systems	system	NOUN
fcis-22002	28	12	often	often	ADV
fcis-22002	28	13	struggle	struggle	VERB
fcis-22002	28	14	to	to	AUX
fcis-22002	28	15	fully	fully	ADV
fcis-22002	28	16	grasp	grasp	VERB
fcis-22002	28	17	domain	domain	NOUN
fcis-22002	28	18	-	-	PUNCT
fcis-22002	28	19	specific	specific	ADJ
fcis-22002	28	20	terminology	terminology	NOUN
fcis-22002	28	21	or	or	CCONJ
fcis-22002	28	22	linguistic	linguistic	ADJ
fcis-22002	28	23	properties	property	NOUN
fcis-22002	28	24	,	,	PUNCT
fcis-22002	28	25	which	which	PRON
fcis-22002	28	26	directly	directly	ADV
fcis-22002	28	27	affects	affect	VERB
fcis-22002	28	28	the	the	DET
fcis-22002	28	29	accuracy	accuracy	NOUN
fcis-22002	28	30	and	and	CCONJ
fcis-22002	28	31	reliability	reliability	NOUN
fcis-22002	28	32	of	of	ADP
fcis-22002	28	33	their	their	PRON
fcis-22002	28	34	application	application	NOUN
fcis-22002	28	35	within	within	ADP
fcis-22002	28	36	the	the	DET
fcis-22002	28	37	speech	speech	NOUN
fcis-22002	28	38	retrieval	retrieval	NOUN
fcis-22002	28	39	domain	domain	NOUN
fcis-22002	28	40	.	.	PUNCT
fcis-22002	29	1	especially	especially	ADV
fcis-22002	29	2	in	in	ADP
fcis-22002	29	3	the	the	DET
fcis-22002	29	4	context	context	NOUN
fcis-22002	29	5	of	of	ADP
fcis-22002	29	6	the	the	DET
fcis-22002	29	7	era	era	NOUN
fcis-22002	29	8	of	of	ADP
fcis-22002	29	9	data	datum	NOUN
fcis-22002	29	10	explosion	explosion	NOUN
fcis-22002	29	11	,	,	PUNCT
fcis-22002	29	12	content	content	NOUN
fcis-22002	29	13	-	-	PUNCT
fcis-22002	29	14	based	base	VERB
fcis-22002	29	15	speech	speech	NOUN
fcis-22002	29	16	retrieval	retrieval	NOUN
fcis-22002	29	17	faces	face	VERB
fcis-22002	29	18	another	another	DET
fcis-22002	29	19	major	major	ADJ
fcis-22002	29	20	challenge	challenge	NOUN
fcis-22002	29	21	the	the	DET
fcis-22002	29	22	efficiency	efficiency	NOUN
fcis-22002	29	23	problem	problem	NOUN
fcis-22002	29	24	.	.	PUNCT
fcis-22002	30	1	[	[	X
fcis-22002	30	2	7	7	NUM
fcis-22002	30	3	-	-	SYM
fcis-22002	30	4	9	9	NUM
fcis-22002	30	5	]	]	PUNCT
fcis-22002	30	6	with	with	ADP
fcis-22002	30	7	the	the	DET
fcis-22002	30	8	rapid	rapid	ADJ
fcis-22002	30	9	growth	growth	NOUN
fcis-22002	30	10	in	in	ADP
fcis-22002	30	11	the	the	DET
fcis-22002	30	12	volume	volume	NOUN
fcis-22002	30	13	of	of	ADP
fcis-22002	30	14	speech	speech	NOUN
fcis-22002	30	15	data	datum	NOUN
fcis-22002	30	16	,	,	PUNCT
fcis-22002	30	17	the	the	DET
fcis-22002	30	18	traditional	traditional	ADJ
fcis-22002	30	19	approach	approach	NOUN
fcis-22002	30	20	of	of	ADP
fcis-22002	30	21	traversing	traverse	VERB
fcis-22002	30	22	the	the	DET
fcis-22002	30	23	entire	entire	ADJ
fcis-22002	30	24	database	database	NOUN
fcis-22002	30	25	to	to	PART
fcis-22002	30	26	match	match	VERB
fcis-22002	30	27	the	the	DET
fcis-22002	30	28	query	query	NOUN
fcis-22002	30	29	data	datum	NOUN
fcis-22002	30	30	becomes	become	VERB
fcis-22002	30	31	extremely	extremely	ADV
fcis-22002	30	32	time	time	NOUN
fcis-22002	30	33	-	-	PUNCT
fcis-22002	30	34	consuming	consume	VERB
fcis-22002	30	35	and	and	CCONJ
fcis-22002	30	36	inefficient	inefficient	ADJ
fcis-22002	30	37	.	.	PUNCT
fcis-22002	31	1	the	the	DET
fcis-22002	31	2	limitations	limitation	NOUN
fcis-22002	31	3	of	of	ADP
fcis-22002	31	4	this	this	DET
fcis-22002	31	5	approach	approach	NOUN
fcis-22002	31	6	are	be	AUX
fcis-22002	31	7	especially	especially	ADV
fcis-22002	31	8	prominent	prominent	ADJ
fcis-22002	31	9	when	when	SCONJ
fcis-22002	31	10	dealing	deal	VERB
fcis-22002	31	11	with	with	ADP
fcis-22002	31	12	large	large	ADJ
fcis-22002	31	13	-	-	PUNCT
fcis-22002	31	14	scale	scale	NOUN
fcis-22002	31	15	datasets	dataset	NOUN
fcis-22002	31	16	.	.	PUNCT
fcis-22002	32	1	to	to	PART
fcis-22002	32	2	address	address	VERB
fcis-22002	32	3	this	this	DET
fcis-22002	32	4	problem	problem	NOUN
fcis-22002	32	5	,	,	PUNCT
fcis-22002	32	6	we	we	PRON
fcis-22002	32	7	introduce	introduce	VERB
fcis-22002	32	8	a	a	DET
fcis-22002	32	9	pre	pre	ADJ
fcis-22002	32	10	-	-	ADJ
fcis-22002	32	11	training	training	NOUN
fcis-22002	32	12	-	-	PUNCT
fcis-22002	32	13	based	base	VERB
fcis-22002	32	14	end	end	NOUN
fcis-22002	32	15	-	-	PUNCT
fcis-22002	32	16	to	to	ADP
fcis-22002	32	17	-	-	PUNCT
fcis-22002	32	18	end	end	NOUN
fcis-22002	32	19	speech	speech	NOUN
fcis-22002	32	20	retrieval	retrieval	NOUN
fcis-22002	32	21	strategy	strategy	NOUN
fcis-22002	32	22	focusing	focus	VERB
fcis-22002	32	23	on	on	ADP
fcis-22002	32	24	acousticlevel	acousticlevel	NOUN
fcis-22002	32	25	sentence	sentence	NOUN
fcis-22002	32	26	matching	matching	NOUN
fcis-22002	32	27	.	.	PUNCT
fcis-22002	33	1	unlike	unlike	ADP
fcis-22002	33	2	traditional	traditional	ADJ
fcis-22002	33	3	retrieval	retrieval	NOUN
fcis-22002	33	4	methods	method	NOUN
fcis-22002	33	5	,	,	PUNCT
fcis-22002	33	6	our	our	PRON
fcis-22002	33	7	approach	approach	NOUN
fcis-22002	33	8	aims	aim	VERB
fcis-22002	33	9	to	to	PART
fcis-22002	33	10	capture	capture	VERB
fcis-22002	33	11	and	and	CCONJ
fcis-22002	33	12	compare	compare	VERB
fcis-22002	33	13	the	the	DET
fcis-22002	33	14	complete	complete	ADJ
fcis-22002	33	15	sentence	sentence	NOUN
fcis-22002	33	16	content	content	NOUN
fcis-22002	33	17	of	of	ADP
fcis-22002	33	18	the	the	DET
fcis-22002	33	19	entire	entire	ADJ
fcis-22002	33	20	speech	speech	NOUN
fcis-22002	33	21	segment	segment	NOUN
fcis-22002	33	22	.	.	PUNCT
fcis-22002	34	1	in	in	ADP
fcis-22002	34	2	order	order	NOUN
fcis-22002	34	3	to	to	PART
fcis-22002	34	4	achieve	achieve	VERB
fcis-22002	34	5	fast	fast	ADJ
fcis-22002	34	6	retrieval	retrieval	NOUN
fcis-22002	34	7	,	,	PUNCT
fcis-22002	34	8	the	the	DET
fcis-22002	34	9	algorithm	algorithm	NOUN
fcis-22002	34	10	also	also	ADV
fcis-22002	34	11	maps	map	VERB
fcis-22002	34	12	the	the	DET
fcis-22002	34	13	entire	entire	ADJ
fcis-22002	34	14	speech	speech	NOUN
fcis-22002	34	15	sentence	sentence	NOUN
fcis-22002	34	16	content	content	NOUN
fcis-22002	34	17	into	into	ADP
fcis-22002	34	18	equal	equal	ADJ
fcis-22002	34	19	-	-	PUNCT
fcis-22002	34	20	length	length	NOUN
fcis-22002	34	21	sequences	sequence	NOUN
fcis-22002	34	22	in	in	ADP
fcis-22002	34	23	hamming	ham	VERB
fcis-22002	34	24	space	space	NOUN
fcis-22002	34	25	,	,	PUNCT
fcis-22002	34	26	constructing	construct	VERB
fcis-22002	34	27	a	a	DET
fcis-22002	34	28	hash	hash	NOUN
fcis-22002	34	29	index	index	NOUN
fcis-22002	34	30	table	table	NOUN
fcis-22002	34	31	for	for	ADP
fcis-22002	34	32	fast	fast	ADJ
fcis-22002	34	33	matching	matching	NOUN
fcis-22002	34	34	in	in	ADP
fcis-22002	34	35	the	the	DET
fcis-22002	34	36	retrieval	retrieval	NOUN
fcis-22002	34	37	task	task	NOUN
fcis-22002	34	38	.	.	PUNCT
fcis-22002	35	1	the	the	DET
fcis-22002	35	2	main	main	ADJ
fcis-22002	35	3	contributions	contribution	NOUN
fcis-22002	35	4	of	of	ADP
fcis-22002	35	5	this	this	DET
fcis-22002	35	6	paper	paper	NOUN
fcis-22002	35	7	can	can	AUX
fcis-22002	35	8	be	be	AUX
fcis-22002	35	9	summarized	summarize	VERB
fcis-22002	35	10	as	as	SCONJ
fcis-22002	35	11	follows	follow	VERB
fcis-22002	35	12	:	:	PUNCT
fcis-22002	35	13	1.to	1.to	NUM
fcis-22002	35	14	circumvent	circumvent	VERB
fcis-22002	35	15	the	the	DET
fcis-22002	35	16	transcription	transcription	NOUN
fcis-22002	35	17	problem	problem	NOUN
fcis-22002	35	18	inherent	inherent	ADJ
fcis-22002	35	19	in	in	ADP
fcis-22002	35	20	traditional	traditional	ADJ
fcis-22002	35	21	cascade	cascade	NOUN
fcis-22002	35	22	speech	speech	NOUN
fcis-22002	35	23	retrieval	retrieval	NOUN
fcis-22002	35	24	,	,	PUNCT
fcis-22002	35	25	we	we	PRON
fcis-22002	35	26	introduce	introduce	VERB
fcis-22002	35	27	an	an	DET
fcis-22002	35	28	end	end	NOUN
fcis-22002	35	29	-	-	PUNCT
fcis-22002	35	30	toend	toend	ADJ
fcis-22002	35	31	acoustic	acoustic	ADJ
fcis-22002	35	32	-	-	PUNCT
fcis-22002	35	33	level	level	NOUN
fcis-22002	35	34	content	content	NOUN
fcis-22002	35	35	matching	matching	NOUN
fcis-22002	35	36	strategy	strategy	NOUN
fcis-22002	35	37	.	.	PUNCT
fcis-22002	36	1	for	for	ADP
fcis-22002	36	2	this	this	DET
fcis-22002	36	3	purpose	purpose	NOUN
fcis-22002	36	4	,	,	PUNCT
fcis-22002	36	5	we	we	PRON
fcis-22002	36	6	use	use	VERB
fcis-22002	36	7	wavenet	wavenet	NOUN
fcis-22002	36	8	as	as	SCONJ
fcis-22002	36	9	the	the	DET
fcis-22002	36	10	encoder	encoder	NOUN
fcis-22002	36	11	of	of	ADP
fcis-22002	36	12	the	the	DET
fcis-22002	36	13	model	model	NOUN
fcis-22002	36	14	and	and	CCONJ
fcis-22002	36	15	pre	pre	ADJ
fcis-22002	36	16	-	-	NOUN
fcis-22002	36	17	train	train	VERB
fcis-22002	36	18	it	it	PRON
fcis-22002	36	19	with	with	ADP
fcis-22002	36	20	ctc	ctc	PROPN
fcis-22002	36	21	speech	speech	NOUN
fcis-22002	36	22	recognition	recognition	NOUN
fcis-22002	36	23	to	to	PART
fcis-22002	36	24	enable	enable	VERB
fcis-22002	36	25	it	it	PRON
fcis-22002	36	26	to	to	ADP
fcis-22002	36	27	pre	pre	VERB
fcis-22002	36	28	-	-	VERB
fcis-22002	36	29	extract	extract	VERB
fcis-22002	36	30	content	content	NOUN
fcis-22002	36	31	-	-	PUNCT
fcis-22002	36	32	related	relate	VERB
fcis-22002	36	33	information	information	NOUN
fcis-22002	36	34	from	from	ADP
fcis-22002	36	35	the	the	DET
fcis-22002	36	36	speech	speech	NOUN
fcis-22002	36	37	signal	signal	NOUN
fcis-22002	36	38	.	.	PUNCT
fcis-22002	37	1	2	2	X
fcis-22002	37	2	.	.	X
fcis-22002	37	3	in	in	ADP
fcis-22002	37	4	order	order	NOUN
fcis-22002	37	5	to	to	PART
fcis-22002	37	6	achieve	achieve	VERB
fcis-22002	37	7	efficient	efficient	ADJ
fcis-22002	37	8	content	content	NOUN
fcis-22002	37	9	retrieval	retrieval	NOUN
fcis-22002	37	10	in	in	ADP
fcis-22002	37	11	large	large	ADJ
fcis-22002	37	12	speech	speech	NOUN
fcis-22002	37	13	databases	database	NOUN
fcis-22002	37	14	,	,	PUNCT
fcis-22002	37	15	we	we	PRON
fcis-22002	37	16	employ	employ	VERB
fcis-22002	37	17	deep	deep	ADJ
fcis-22002	37	18	hashing	hashing	NOUN
fcis-22002	37	19	techniques	technique	NOUN
fcis-22002	37	20	.	.	PUNCT
fcis-22002	38	1	relying	rely	VERB
fcis-22002	38	2	on	on	ADP
fcis-22002	38	3	the	the	DET
fcis-22002	38	4	powerful	powerful	ADJ
fcis-22002	38	5	characterization	characterization	NOUN
fcis-22002	38	6	capability	capability	NOUN
fcis-22002	38	7	of	of	ADP
fcis-22002	38	8	deep	deep	ADJ
fcis-22002	38	9	learning	learning	NOUN
fcis-22002	38	10	,	,	PUNCT
fcis-22002	38	11	by	by	ADP
fcis-22002	38	12	projecting	project	VERB
fcis-22002	38	13	the	the	DET
fcis-22002	38	14	whole	whole	ADJ
fcis-22002	38	15	speech	speech	NOUN
fcis-22002	38	16	content	content	NOUN
fcis-22002	38	17	directly	directly	ADV
fcis-22002	38	18	into	into	ADP
fcis-22002	38	19	to	to	ADP
fcis-22002	38	20	the	the	DET
fcis-22002	38	21	hamming	hamming	NOUN
fcis-22002	38	22	space	space	NOUN
fcis-22002	38	23	and	and	CCONJ
fcis-22002	38	24	comparing	compare	VERB
fcis-22002	38	25	the	the	DET
fcis-22002	38	26	hamming	hamming	ADJ
fcis-22002	38	27	distances	distance	NOUN
fcis-22002	38	28	,	,	PUNCT
fcis-22002	38	29	our	our	PRON
fcis-22002	38	30	method	method	NOUN
fcis-22002	38	31	significantly	significantly	ADV
fcis-22002	38	32	improves	improve	VERB
fcis-22002	38	33	the	the	DET
fcis-22002	38	34	retrieval	retrieval	NOUN
fcis-22002	38	35	efficiency	efficiency	NOUN
fcis-22002	38	36	compared	compare	VERB
fcis-22002	38	37	to	to	ADP
fcis-22002	38	38	direct	direct	ADJ
fcis-22002	38	39	speech	speech	NOUN
fcis-22002	38	40	matching	matching	NOUN
fcis-22002	38	41	.	.	PUNCT
fcis-22002	39	1	the	the	DET
fcis-22002	39	2	rest	rest	NOUN
fcis-22002	39	3	of	of	ADP
fcis-22002	39	4	the	the	DET
fcis-22002	39	5	paper	paper	NOUN
fcis-22002	39	6	is	be	AUX
fcis-22002	39	7	organized	organize	VERB
fcis-22002	39	8	as	as	SCONJ
fcis-22002	39	9	follows	follow	VERB
fcis-22002	39	10	:	:	PUNCT
fcis-22002	39	11	in	in	ADP
fcis-22002	39	12	section	section	NOUN
fcis-22002	39	13	2	2	NUM
fcis-22002	39	14	,	,	PUNCT
fcis-22002	39	15	we	we	PRON
fcis-22002	39	16	introduce	introduce	VERB
fcis-22002	39	17	the	the	DET
fcis-22002	39	18	technical	technical	ADJ
fcis-22002	39	19	issues	issue	NOUN
fcis-22002	39	20	related	relate	VERB
fcis-22002	39	21	to	to	ADP
fcis-22002	39	22	the	the	DET
fcis-22002	39	23	algorithm	algorithm	NOUN
fcis-22002	39	24	.	.	PUNCT
fcis-22002	40	1	23	23	NUM
fcis-22002	40	2	section	section	NOUN
fcis-22002	40	3	3	3	NUM
fcis-22002	40	4	provides	provide	VERB
fcis-22002	40	5	a	a	DET
fcis-22002	40	6	detailed	detailed	ADJ
fcis-22002	40	7	description	description	NOUN
fcis-22002	40	8	of	of	ADP
fcis-22002	40	9	the	the	DET
fcis-22002	40	10	algorithm	algorithm	NOUN
fcis-22002	40	11	.	.	PUNCT
fcis-22002	41	1	in	in	ADP
fcis-22002	41	2	section	section	NOUN
fcis-22002	41	3	4	4	NUM
fcis-22002	41	4	we	we	PRON
fcis-22002	41	5	present	present	VERB
fcis-22002	41	6	the	the	DET
fcis-22002	41	7	experimental	experimental	ADJ
fcis-22002	41	8	dataset	dataset	NOUN
fcis-22002	41	9	and	and	CCONJ
fcis-22002	41	10	setup	setup	NOUN
fcis-22002	41	11	.	.	PUNCT
fcis-22002	42	1	finally	finally	ADV
fcis-22002	42	2	,	,	PUNCT
fcis-22002	42	3	section	section	NOUN
fcis-22002	42	4	5	5	NUM
fcis-22002	42	5	summarizes	summarize	NOUN
fcis-22002	42	6	the	the	DET
fcis-22002	42	7	proposed	propose	VERB
fcis-22002	42	8	work	work	NOUN
fcis-22002	42	9	.	.	PUNCT
fcis-22002	43	1	2	2	X
fcis-22002	43	2	.	.	X
fcis-22002	43	3	related	relate	VERB
fcis-22002	43	4	works	work	NOUN
fcis-22002	43	5	2.1	2.1	NUM
fcis-22002	43	6	.	.	PUNCT
fcis-22002	44	1	deep	deep	ADJ
fcis-22002	44	2	hashing	hashing	NOUN
fcis-22002	44	3	in	in	ADP
fcis-22002	44	4	the	the	DET
fcis-22002	44	5	task	task	NOUN
fcis-22002	44	6	of	of	ADP
fcis-22002	44	7	nearest	near	ADJ
fcis-22002	44	8	neighbor	neighbor	NOUN
fcis-22002	44	9	search	search	NOUN
fcis-22002	44	10	,	,	PUNCT
fcis-22002	44	11	hashing	hashing	NOUN
fcis-22002	44	12	methods	method	NOUN
fcis-22002	44	13	are	be	AUX
fcis-22002	44	14	widely	widely	ADV
fcis-22002	44	15	adopted	adopt	VERB
fcis-22002	44	16	for	for	ADP
fcis-22002	44	17	their	their	PRON
fcis-22002	44	18	excellent	excellent	ADJ
fcis-22002	44	19	computational	computational	ADJ
fcis-22002	44	20	and	and	CCONJ
fcis-22002	44	21	storage	storage	NOUN
fcis-22002	44	22	efficiency	efficiency	NOUN
fcis-22002	44	23	.	.	PUNCT
fcis-22002	45	1	the	the	DET
fcis-22002	45	2	goal	goal	NOUN
fcis-22002	45	3	is	be	AUX
fcis-22002	45	4	to	to	PART
fcis-22002	45	5	convert	convert	VERB
fcis-22002	45	6	high	high	ADJ
fcis-22002	45	7	-	-	PUNCT
fcis-22002	45	8	dimensional	dimensional	ADJ
fcis-22002	45	9	feature	feature	NOUN
fcis-22002	45	10	vectors	vector	NOUN
fcis-22002	45	11	into	into	ADP
fcis-22002	45	12	binary	binary	ADJ
fcis-22002	45	13	hash	hash	NOUN
fcis-22002	45	14	codes	code	NOUN
fcis-22002	45	15	,	,	PUNCT
fcis-22002	45	16	ensuring	ensure	VERB
fcis-22002	45	17	that	that	SCONJ
fcis-22002	45	18	the	the	DET
fcis-22002	45	19	hash	hash	NOUN
fcis-22002	45	20	codes	code	NOUN
fcis-22002	45	21	of	of	ADP
fcis-22002	45	22	similar	similar	ADJ
fcis-22002	45	23	data	datum	NOUN
fcis-22002	45	24	are	be	AUX
fcis-22002	45	25	as	as	ADV
fcis-22002	45	26	close	close	ADJ
fcis-22002	45	27	as	as	ADP
fcis-22002	45	28	possible	possible	ADJ
fcis-22002	45	29	.	.	PUNCT
fcis-22002	46	1	traditional	traditional	ADJ
fcis-22002	46	2	hashing	hashing	NOUN
fcis-22002	46	3	algorithms	algorithm	NOUN
fcis-22002	46	4	usually	usually	ADV
fcis-22002	46	5	consist	consist	VERB
fcis-22002	46	6	of	of	ADP
fcis-22002	46	7	two	two	NUM
fcis-22002	46	8	parts	part	NOUN
fcis-22002	46	9	:	:	PUNCT
fcis-22002	46	10	a	a	DET
fcis-22002	46	11	mapping	mapping	NOUN
fcis-22002	46	12	function	function	NOUN
fcis-22002	46	13	to	to	PART
fcis-22002	46	14	handle	handle	VERB
fcis-22002	46	15	the	the	DET
fcis-22002	46	16	mapping	mapping	NOUN
fcis-22002	46	17	of	of	ADP
fcis-22002	46	18	the	the	DET
fcis-22002	46	19	input	input	NOUN
fcis-22002	46	20	data	datum	NOUN
fcis-22002	46	21	to	to	ADP
fcis-22002	46	22	the	the	DET
fcis-22002	46	23	hash	hash	NOUN
fcis-22002	46	24	code	code	NOUN
fcis-22002	46	25	,	,	PUNCT
fcis-22002	46	26	and	and	CCONJ
fcis-22002	46	27	a	a	DET
fcis-22002	46	28	key	key	ADJ
fcis-22002	46	29	-	-	PUNCT
fcis-22002	46	30	value	value	NOUN
fcis-22002	46	31	storage	storage	NOUN
fcis-22002	46	32	table	table	NOUN
fcis-22002	46	33	to	to	PART
fcis-22002	46	34	store	store	VERB
fcis-22002	46	35	the	the	DET
fcis-22002	46	36	hash	hash	NOUN
fcis-22002	46	37	code	code	NOUN
fcis-22002	46	38	for	for	ADP
fcis-22002	46	39	easy	easy	ADJ
fcis-22002	46	40	lookup	lookup	NOUN
fcis-22002	46	41	.	.	PUNCT
fcis-22002	47	1	in	in	ADP
fcis-22002	47	2	the	the	DET
fcis-22002	47	3	field	field	NOUN
fcis-22002	47	4	of	of	ADP
fcis-22002	47	5	speech	speech	NOUN
fcis-22002	47	6	retrieval	retrieval	NOUN
fcis-22002	47	7	,	,	PUNCT
fcis-22002	47	8	perceptual	perceptual	ADJ
fcis-22002	47	9	hashing	hashing	NOUN
fcis-22002	47	10	techniques	technique	NOUN
fcis-22002	47	11	are	be	AUX
fcis-22002	47	12	widely	widely	ADV
fcis-22002	47	13	used	use	VERB
fcis-22002	47	14	.	.	PUNCT
fcis-22002	48	1	the	the	DET
fcis-22002	48	2	core	core	ADJ
fcis-22002	48	3	idea	idea	NOUN
fcis-22002	48	4	of	of	ADP
fcis-22002	48	5	perceptual	perceptual	ADJ
fcis-22002	48	6	hashing	hashing	NOUN
fcis-22002	48	7	is	be	AUX
fcis-22002	48	8	to	to	PART
fcis-22002	48	9	capture	capture	VERB
fcis-22002	48	10	the	the	DET
fcis-22002	48	11	perceptual	perceptual	ADJ
fcis-22002	48	12	features	feature	NOUN
fcis-22002	48	13	of	of	ADP
fcis-22002	48	14	the	the	DET
fcis-22002	48	15	speech	speech	NOUN
fcis-22002	48	16	signal	signal	NOUN
fcis-22002	48	17	and	and	CCONJ
fcis-22002	48	18	encode	encode	VERB
fcis-22002	48	19	them	they	PRON
fcis-22002	48	20	into	into	ADP
fcis-22002	48	21	a	a	DET
fcis-22002	48	22	binary	binary	ADJ
fcis-22002	48	23	hash	hash	NOUN
fcis-22002	48	24	code	code	NOUN
fcis-22002	48	25	,	,	PUNCT
fcis-22002	48	26	whose	whose	DET
fcis-22002	48	27	hash	hash	NOUN
fcis-22002	48	28	function	function	NOUN
fcis-22002	48	29	is	be	AUX
fcis-22002	48	30	the	the	DET
fcis-22002	48	31	perceptual	perceptual	ADJ
fcis-22002	48	32	features	feature	NOUN
fcis-22002	48	33	of	of	ADP
fcis-22002	48	34	the	the	DET
fcis-22002	48	35	speech	speech	NOUN
fcis-22002	48	36	(	(	PUNCT
fcis-22002	48	37	timbre	timbre	NOUN
fcis-22002	48	38	,	,	PUNCT
fcis-22002	48	39	prosody	prosody	NOUN
fcis-22002	48	40	,	,	PUNCT
fcis-22002	48	41	resonance	resonance	NOUN
fcis-22002	48	42	features	feature	NOUN
fcis-22002	48	43	,	,	PUNCT
fcis-22002	48	44	etc	etc	X
fcis-22002	48	45	.	.	X
fcis-22002	48	46	)	)	PUNCT
fcis-22002	48	47	.	.	PUNCT
fcis-22002	49	1	however	however	ADV
fcis-22002	49	2	,	,	PUNCT
fcis-22002	49	3	since	since	SCONJ
fcis-22002	49	4	perceptual	perceptual	ADJ
fcis-22002	49	5	hashing	hashing	NOUN
fcis-22002	49	6	mainly	mainly	ADV
fcis-22002	49	7	focuses	focus	VERB
fcis-22002	49	8	on	on	ADP
fcis-22002	49	9	the	the	DET
fcis-22002	49	10	low	low	ADJ
fcis-22002	49	11	-	-	PUNCT
fcis-22002	49	12	level	level	NOUN
fcis-22002	49	13	perceptual	perceptual	ADJ
fcis-22002	49	14	features	feature	NOUN
fcis-22002	49	15	of	of	ADP
fcis-22002	49	16	speech	speech	NOUN
fcis-22002	49	17	rather	rather	ADV
fcis-22002	49	18	than	than	ADP
fcis-22002	49	19	its	its	PRON
fcis-22002	49	20	high	high	ADJ
fcis-22002	49	21	-	-	PUNCT
fcis-22002	49	22	level	level	NOUN
fcis-22002	49	23	semantic	semantic	ADJ
fcis-22002	49	24	content	content	NOUN
fcis-22002	49	25	,	,	PUNCT
fcis-22002	49	26	it	it	PRON
fcis-22002	49	27	is	be	AUX
fcis-22002	49	28	not	not	PART
fcis-22002	49	29	suitable	suitable	ADJ
fcis-22002	49	30	for	for	ADP
fcis-22002	49	31	applications	application	NOUN
fcis-22002	49	32	that	that	PRON
fcis-22002	49	33	require	require	VERB
fcis-22002	49	34	in	in	ADP
fcis-22002	49	35	-	-	PUNCT
fcis-22002	49	36	depth	depth	NOUN
fcis-22002	49	37	understanding	understanding	NOUN
fcis-22002	49	38	of	of	ADP
fcis-22002	49	39	speech	speech	NOUN
fcis-22002	49	40	content	content	NOUN
fcis-22002	49	41	.	.	PUNCT
fcis-22002	50	1	to	to	PART
fcis-22002	50	2	solve	solve	VERB
fcis-22002	50	3	this	this	DET
fcis-22002	50	4	problem	problem	NOUN
fcis-22002	50	5	,	,	PUNCT
fcis-22002	50	6	deep	deep	ADJ
fcis-22002	50	7	hashing	hashing	NOUN
fcis-22002	50	8	techniques	technique	NOUN
fcis-22002	50	9	,	,	PUNCT
fcis-22002	50	10	which	which	PRON
fcis-22002	50	11	apply	apply	VERB
fcis-22002	50	12	deep	deep	ADJ
fcis-22002	50	13	learning	learning	NOUN
fcis-22002	50	14	to	to	ADP
fcis-22002	50	15	hashing	hash	VERB
fcis-22002	50	16	algorithms	algorithm	NOUN
fcis-22002	50	17	,	,	PUNCT
fcis-22002	50	18	are	be	AUX
fcis-22002	50	19	beginning	begin	VERB
fcis-22002	50	20	to	to	PART
fcis-22002	50	21	receive	receive	VERB
fcis-22002	50	22	attention	attention	NOUN
fcis-22002	50	23	from	from	ADP
fcis-22002	50	24	researchers	researcher	NOUN
fcis-22002	50	25	.	.	PUNCT
fcis-22002	51	1	deep	deep	ADJ
fcis-22002	51	2	neural	neural	ADJ
fcis-22002	51	3	networks	network	NOUN
fcis-22002	51	4	can	can	AUX
fcis-22002	51	5	learn	learn	VERB
fcis-22002	51	6	a	a	DET
fcis-22002	51	7	set	set	NOUN
fcis-22002	51	8	of	of	ADP
fcis-22002	51	9	hash	hash	NOUN
fcis-22002	51	10	functions	function	NOUN
fcis-22002	51	11	customized	customize	VERB
fcis-22002	51	12	for	for	ADP
fcis-22002	51	13	a	a	DET
fcis-22002	51	14	specific	specific	ADJ
fcis-22002	51	15	task	task	NOUN
fcis-22002	51	16	.	.	PUNCT
fcis-22002	52	1	ideally	ideally	ADV
fcis-22002	52	2	,	,	PUNCT
fcis-22002	52	3	a	a	DET
fcis-22002	52	4	deep	deep	ADJ
fcis-22002	52	5	hash	hash	NOUN
fcis-22002	52	6	network	network	NOUN
fcis-22002	52	7	preserves	preserve	VERB
fcis-22002	52	8	semantic	semantic	ADJ
fcis-22002	52	9	similarity	similarity	NOUN
fcis-22002	52	10	information	information	NOUN
fcis-22002	52	11	of	of	ADP
fcis-22002	52	12	multimedia	multimedia	NOUN
fcis-22002	52	13	data	datum	NOUN
fcis-22002	52	14	during	during	ADP
fcis-22002	52	15	hash	hash	NOUN
fcis-22002	52	16	construction	construction	NOUN
fcis-22002	52	17	.	.	PUNCT
fcis-22002	53	1	deep	deep	ADJ
fcis-22002	53	2	hashing	hashing	NOUN
fcis-22002	53	3	has	have	VERB
fcis-22002	53	4	higher	high	ADJ
fcis-22002	53	5	accuracy	accuracy	NOUN
fcis-22002	53	6	,	,	PUNCT
fcis-22002	53	7	better	well	ADJ
fcis-22002	53	8	generalization	generalization	NOUN
fcis-22002	53	9	ability	ability	NOUN
fcis-22002	53	10	and	and	CCONJ
fcis-22002	53	11	stronger	strong	ADJ
fcis-22002	53	12	robustness	robustness	NOUN
fcis-22002	53	13	in	in	ADP
fcis-22002	53	14	image	image	NOUN
fcis-22002	53	15	retrieval	retrieval	NOUN
fcis-22002	53	16	than	than	ADP
fcis-22002	53	17	traditional	traditional	ADJ
fcis-22002	53	18	feature	feature	NOUN
fcis-22002	53	19	-	-	PUNCT
fcis-22002	53	20	based	base	VERB
fcis-22002	53	21	manual	manual	ADJ
fcis-22002	53	22	hashing	hashing	NOUN
fcis-22002	53	23	methods	method	NOUN
fcis-22002	53	24	[	[	X
fcis-22002	53	25	10	10	NUM
fcis-22002	53	26	-	-	SYM
fcis-22002	53	27	13	13	NUM
fcis-22002	53	28	]	]	PUNCT
fcis-22002	53	29	.	.	PUNCT
fcis-22002	54	1	with	with	ADP
fcis-22002	54	2	the	the	DET
fcis-22002	54	3	successful	successful	ADJ
fcis-22002	54	4	application	application	NOUN
fcis-22002	54	5	of	of	ADP
fcis-22002	54	6	deep	deep	ADJ
fcis-22002	54	7	hashing	hashing	NOUN
fcis-22002	54	8	in	in	ADP
fcis-22002	54	9	the	the	DET
fcis-22002	54	10	image	image	NOUN
fcis-22002	54	11	domain	domain	NOUN
fcis-22002	54	12	,	,	PUNCT
fcis-22002	54	13	researchers	researcher	NOUN
fcis-22002	54	14	have	have	AUX
fcis-22002	54	15	begun	begin	VERB
fcis-22002	54	16	to	to	PART
fcis-22002	54	17	explore	explore	VERB
fcis-22002	54	18	its	its	PRON
fcis-22002	54	19	potential	potential	NOUN
fcis-22002	54	20	in	in	ADP
fcis-22002	54	21	speech	speech	NOUN
fcis-22002	54	22	retrieval	retrieval	NOUN
fcis-22002	54	23	.	.	PUNCT
fcis-22002	55	1	zhang	zhang	PROPN
fcis-22002	55	2	et	et	PROPN
fcis-22002	55	3	al	al	PROPN
fcis-22002	55	4	.	.	PROPN
fcis-22002	56	1	used	use	VERB
fcis-22002	56	2	neural	neural	ADJ
fcis-22002	56	3	networks	network	NOUN
fcis-22002	56	4	to	to	PART
fcis-22002	56	5	learn	learn	VERB
fcis-22002	56	6	depth	depth	NOUN
fcis-22002	56	7	-	-	PUNCT
fcis-22002	56	8	aware	aware	ADJ
fcis-22002	56	9	features	feature	NOUN
fcis-22002	56	10	and	and	CCONJ
fcis-22002	56	11	generate	generate	VERB
fcis-22002	56	12	depth	depth	NOUN
fcis-22002	56	13	-	-	PUNCT
fcis-22002	56	14	aware	aware	ADJ
fcis-22002	56	15	hash	hash	NOUN
fcis-22002	56	16	sequences	sequence	NOUN
fcis-22002	56	17	to	to	PART
fcis-22002	56	18	achieve	achieve	VERB
fcis-22002	56	19	efficient	efficient	ADJ
fcis-22002	56	20	speech	speech	NOUN
fcis-22002	56	21	retrieval	retrieval	NOUN
fcis-22002	56	22	with	with	ADP
fcis-22002	56	23	good	good	ADJ
fcis-22002	56	24	discriminative	discriminative	NOUN
fcis-22002	56	25	and	and	CCONJ
fcis-22002	56	26	robustness	robustness	NOUN
fcis-22002	56	27	[	[	X
fcis-22002	56	28	14].yuan	14].yuan	PROPN
fcis-22002	56	29	et	et	NOUN
fcis-22002	56	30	al	al	PROPN
fcis-22002	56	31	.	.	PROPN
fcis-22002	56	32	used	use	VERB
fcis-22002	56	33	deep	deep	ADJ
fcis-22002	56	34	hash	hash	NOUN
fcis-22002	56	35	code	code	NOUN
fcis-22002	56	36	embedding	embed	VERB
fcis-22002	56	37	instead	instead	ADV
fcis-22002	56	38	of	of	ADP
fcis-22002	56	39	a	a	DET
fcis-22002	56	40	large	large	ADJ
fcis-22002	56	41	number	number	NOUN
fcis-22002	56	42	of	of	ADP
fcis-22002	56	43	awes	awe	NOUN
fcis-22002	56	44	to	to	PART
fcis-22002	56	45	achieve	achieve	VERB
fcis-22002	56	46	keyword	keyword	NOUN
fcis-22002	56	47	-	-	PUNCT
fcis-22002	56	48	based	base	VERB
fcis-22002	56	49	of	of	ADP
fcis-22002	56	50	fast	fast	ADJ
fcis-22002	56	51	qbe	qbe	PROPN
fcis-22002	56	52	speech	speech	NOUN
fcis-22002	56	53	retrieval	retrieval	NOUN
fcis-22002	57	1	[	[	X
fcis-22002	57	2	15	15	NUM
fcis-22002	57	3	]	]	PUNCT
fcis-22002	57	4	.	.	PUNCT
fcis-22002	58	1	our	our	PRON
fcis-22002	58	2	content	content	NOUN
fcis-22002	58	3	-	-	PUNCT
fcis-22002	58	4	based	base	VERB
fcis-22002	58	5	qbe	qbe	PROPN
fcis-22002	58	6	speech	speech	NOUN
fcis-22002	58	7	retrieval	retrieval	NOUN
fcis-22002	58	8	algorithm	algorithm	NOUN
fcis-22002	58	9	is	be	AUX
fcis-22002	58	10	similar	similar	ADJ
fcis-22002	58	11	to	to	ADP
fcis-22002	58	12	the	the	DET
fcis-22002	58	13	sentence	sentence	NOUN
fcis-22002	58	14	embedding	embed	VERB
fcis-22002	58	15	technique	technique	NOUN
fcis-22002	58	16	that	that	PRON
fcis-22002	58	17	transforms	transform	VERB
fcis-22002	58	18	sentences	sentence	NOUN
fcis-22002	58	19	or	or	CCONJ
fcis-22002	58	20	text	text	NOUN
fcis-22002	58	21	passages	passage	NOUN
fcis-22002	58	22	into	into	ADP
fcis-22002	58	23	fixed	fix	VERB
fcis-22002	58	24	-	-	PUNCT
fcis-22002	58	25	size	size	NOUN
fcis-22002	58	26	vectors	vector	NOUN
fcis-22002	58	27	by	by	ADP
fcis-22002	58	28	mapping	map	VERB
fcis-22002	58	29	the	the	DET
fcis-22002	58	30	contents	content	NOUN
fcis-22002	58	31	of	of	ADP
fcis-22002	58	32	speech	speech	NOUN
fcis-22002	58	33	sentences	sentence	NOUN
fcis-22002	58	34	into	into	ADP
fcis-22002	58	35	hamming	hamming	NOUN
fcis-22002	58	36	space	space	NOUN
fcis-22002	58	37	and	and	CCONJ
fcis-22002	58	38	matching	match	VERB
fcis-22002	58	39	them	they	PRON
fcis-22002	58	40	by	by	ADP
fcis-22002	58	41	a	a	DET
fcis-22002	58	42	normalized	normalize	VERB
fcis-22002	58	43	hamming	hamming	NOUN
fcis-22002	58	44	distance	distance	NOUN
fcis-22002	58	45	algorithm	algorithm	NOUN
fcis-22002	58	46	.	.	PUNCT
fcis-22002	59	1	2.2	2.2	NUM
fcis-22002	59	2	.	.	PUNCT
fcis-22002	59	3	content	content	NOUN
fcis-22002	59	4	-	-	PUNCT
fcis-22002	59	5	based	base	VERB
fcis-22002	59	6	speech	speech	NOUN
fcis-22002	59	7	retrieval	retrieval	NOUN
fcis-22002	59	8	the	the	DET
fcis-22002	59	9	objective	objective	NOUN
fcis-22002	59	10	of	of	ADP
fcis-22002	59	11	the	the	DET
fcis-22002	59	12	speech	speech	NOUN
fcis-22002	59	13	retrieval	retrieval	NOUN
fcis-22002	59	14	task	task	NOUN
fcis-22002	59	15	is	be	AUX
fcis-22002	59	16	to	to	PART
fcis-22002	59	17	identify	identify	VERB
fcis-22002	59	18	speeches	speech	NOUN
fcis-22002	59	19	with	with	ADP
fcis-22002	59	20	content	content	NOUN
fcis-22002	59	21	identical	identical	ADJ
fcis-22002	59	22	to	to	ADP
fcis-22002	59	23	a	a	DET
fcis-22002	59	24	given	give	VERB
fcis-22002	59	25	speech	speech	NOUN
fcis-22002	59	26	sample	sample	NOUN
fcis-22002	59	27	.	.	PUNCT
fcis-22002	60	1	traditional	traditional	ADJ
fcis-22002	60	2	methods[16	methods[16	NOUN
fcis-22002	60	3	]	]	PUNCT
fcis-22002	60	4	primarily	primarily	ADV
fcis-22002	60	5	rely	rely	VERB
fcis-22002	60	6	on	on	ADP
fcis-22002	60	7	asr	asr	PROPN
fcis-22002	60	8	technology	technology	NOUN
fcis-22002	60	9	to	to	PART
fcis-22002	60	10	transcribe	transcribe	VERB
fcis-22002	60	11	speech	speech	NOUN
fcis-22002	60	12	into	into	ADP
fcis-22002	60	13	text	text	NOUN
fcis-22002	60	14	,	,	PUNCT
fcis-22002	60	15	followed	follow	VERB
fcis-22002	60	16	by	by	ADP
fcis-22002	60	17	comparative	comparative	ADJ
fcis-22002	60	18	retrieval	retrieval	NOUN
fcis-22002	60	19	.	.	PUNCT
fcis-22002	61	1	this	this	DET
fcis-22002	61	2	approach	approach	NOUN
fcis-22002	61	3	typically	typically	ADV
fcis-22002	61	4	involves	involve	VERB
fcis-22002	61	5	two	two	NUM
fcis-22002	61	6	primary	primary	ADJ
fcis-22002	61	7	steps	step	NOUN
fcis-22002	61	8	:	:	PUNCT
fcis-22002	61	9	initially	initially	ADV
fcis-22002	61	10	,	,	PUNCT
fcis-22002	61	11	the	the	DET
fcis-22002	61	12	entire	entire	ADJ
fcis-22002	61	13	speech	speech	NOUN
fcis-22002	61	14	database	database	NOUN
fcis-22002	61	15	is	be	AUX
fcis-22002	61	16	transcribed	transcribe	VERB
fcis-22002	61	17	into	into	ADP
fcis-22002	61	18	text	text	NOUN
fcis-22002	61	19	using	use	VERB
fcis-22002	61	20	asr	asr	NOUN
fcis-22002	61	21	technology	technology	NOUN
fcis-22002	61	22	;	;	PUNCT
fcis-22002	61	23	subsequently	subsequently	ADV
fcis-22002	61	24	,	,	PUNCT
fcis-22002	61	25	text	text	NOUN
fcis-22002	61	26	retrieval	retrieval	NOUN
fcis-22002	61	27	techniques	technique	NOUN
fcis-22002	61	28	are	be	AUX
fcis-22002	61	29	employed	employ	VERB
fcis-22002	61	30	to	to	PART
fcis-22002	61	31	search	search	VERB
fcis-22002	61	32	for	for	ADP
fcis-22002	61	33	specific	specific	ADJ
fcis-22002	61	34	words	word	NOUN
fcis-22002	61	35	or	or	CCONJ
fcis-22002	61	36	phrases	phrase	NOUN
fcis-22002	61	37	within	within	ADP
fcis-22002	61	38	the	the	DET
fcis-22002	61	39	transcribed	transcribe	VERB
fcis-22002	61	40	text	text	NOUN
fcis-22002	61	41	.	.	PUNCT
fcis-22002	62	1	a	a	DET
fcis-22002	62	2	significant	significant	ADJ
fcis-22002	62	3	advantage	advantage	NOUN
fcis-22002	62	4	of	of	ADP
fcis-22002	62	5	this	this	DET
fcis-22002	62	6	method	method	NOUN
fcis-22002	62	7	is	be	AUX
fcis-22002	62	8	its	its	PRON
fcis-22002	62	9	ability	ability	NOUN
fcis-22002	62	10	to	to	PART
fcis-22002	62	11	swiftly	swiftly	ADV
fcis-22002	62	12	search	search	VERB
fcis-22002	62	13	within	within	ADP
fcis-22002	62	14	the	the	DET
fcis-22002	62	15	speech	speech	NOUN
fcis-22002	62	16	database	database	NOUN
fcis-22002	62	17	.	.	PUNCT
fcis-22002	63	1	however	however	ADV
fcis-22002	63	2	,	,	PUNCT
fcis-22002	63	3	a	a	DET
fcis-22002	63	4	pronounced	pronounce	VERB
fcis-22002	63	5	drawback	drawback	NOUN
fcis-22002	63	6	is	be	AUX
fcis-22002	63	7	its	its	PRON
fcis-22002	63	8	total	total	ADJ
fcis-22002	63	9	dependence	dependence	NOUN
fcis-22002	63	10	on	on	ADP
fcis-22002	63	11	the	the	DET
fcis-22002	63	12	accuracy	accuracy	NOUN
fcis-22002	63	13	of	of	ADP
fcis-22002	63	14	asr	asr	NOUN
fcis-22002	63	15	.	.	PUNCT
fcis-22002	64	1	consequently	consequently	ADV
fcis-22002	64	2	,	,	PUNCT
fcis-22002	64	3	if	if	SCONJ
fcis-22002	64	4	there	there	PRON
fcis-22002	64	5	are	be	VERB
fcis-22002	64	6	errors	error	NOUN
fcis-22002	64	7	in	in	ADP
fcis-22002	64	8	the	the	DET
fcis-22002	64	9	asr	asr	NOUN
fcis-22002	64	10	transcription	transcription	NOUN
fcis-22002	64	11	or	or	CCONJ
fcis-22002	64	12	if	if	SCONJ
fcis-22002	64	13	the	the	DET
fcis-22002	64	14	queried	query	VERB
fcis-22002	64	15	vocabulary	vocabulary	NOUN
fcis-22002	64	16	is	be	AUX
fcis-22002	64	17	absent	absent	ADJ
fcis-22002	64	18	from	from	ADP
fcis-22002	64	19	the	the	DET
fcis-22002	64	20	asr	asr	PROPN
fcis-22002	64	21	lexicon	lexicon	NOUN
fcis-22002	64	22	(	(	PUNCT
fcis-22002	64	23	i.e.	i.e.	X
fcis-22002	64	24	,	,	PUNCT
fcis-22002	64	25	oov	oov	PROPN
fcis-22002	64	26	terms	term	NOUN
fcis-22002	64	27	)	)	PUNCT
fcis-22002	64	28	,	,	PUNCT
fcis-22002	64	29	the	the	DET
fcis-22002	64	30	retrieval	retrieval	NOUN
fcis-22002	64	31	performance	performance	NOUN
fcis-22002	64	32	might	might	AUX
fcis-22002	64	33	be	be	AUX
fcis-22002	64	34	severely	severely	ADV
fcis-22002	64	35	compromised	compromise	VERB
fcis-22002	64	36	.	.	PUNCT
fcis-22002	65	1	to	to	PART
fcis-22002	65	2	address	address	VERB
fcis-22002	65	3	the	the	DET
fcis-22002	65	4	matching	matching	NOUN
fcis-22002	65	5	challenge	challenge	NOUN
fcis-22002	65	6	at	at	ADP
fcis-22002	65	7	the	the	DET
fcis-22002	65	8	acoustic	acoustic	ADJ
fcis-22002	65	9	level	level	NOUN
fcis-22002	65	10	,	,	PUNCT
fcis-22002	65	11	researchers	researcher	NOUN
fcis-22002	65	12	introduced	introduce	VERB
fcis-22002	65	13	the	the	DET
fcis-22002	65	14	dtw	dtw	PROPN
fcis-22002	65	15	method	method	NOUN
fcis-22002	65	16	.	.	PUNCT
fcis-22002	66	1	for	for	ADP
fcis-22002	66	2	instance	instance	NOUN
fcis-22002	66	3	,	,	PUNCT
fcis-22002	66	4	in	in	ADP
fcis-22002	66	5	the	the	DET
fcis-22002	66	6	research	research	NOUN
fcis-22002	66	7	by	by	ADP
fcis-22002	66	8	muaidi	muaidi	NOUN
fcis-22002	66	9	et	et	NOUN
fcis-22002	66	10	al.[17	al.[17	PROPN
fcis-22002	66	11	]	]	PUNCT
fcis-22002	66	12	,	,	PUNCT
fcis-22002	66	13	dtw	dtw	PROPN
fcis-22002	66	14	was	be	AUX
fcis-22002	66	15	utilized	utilize	VERB
fcis-22002	66	16	for	for	ADP
fcis-22002	66	17	arabic	arabic	ADJ
fcis-22002	66	18	audio	audio	NOUN
fcis-22002	66	19	news	news	NOUN
fcis-22002	66	20	retrieval	retrieval	NOUN
fcis-22002	66	21	.	.	PUNCT
fcis-22002	67	1	dtw	dtw	PROPN
fcis-22002	67	2	allows	allow	VERB
fcis-22002	67	3	a	a	DET
fcis-22002	67	4	nonlinear	nonlinear	ADJ
fcis-22002	67	5	mapping	mapping	NOUN
fcis-22002	67	6	between	between	ADP
fcis-22002	67	7	two	two	NUM
fcis-22002	67	8	speech	speech	NOUN
fcis-22002	67	9	signals	signal	NOUN
fcis-22002	67	10	to	to	PART
fcis-22002	67	11	find	find	VERB
fcis-22002	67	12	the	the	DET
fcis-22002	67	13	minimum	minimum	ADJ
fcis-22002	67	14	distance	distance	NOUN
fcis-22002	67	15	between	between	ADP
fcis-22002	67	16	them	they	PRON
fcis-22002	67	17	.	.	PUNCT
fcis-22002	68	1	although	although	SCONJ
fcis-22002	68	2	the	the	DET
fcis-22002	68	3	dtw	dtw	PROPN
fcis-22002	68	4	method	method	NOUN
fcis-22002	68	5	has	have	AUX
fcis-22002	68	6	facilitated	facilitate	VERB
fcis-22002	68	7	end	end	NOUN
fcis-22002	68	8	-	-	PUNCT
fcis-22002	68	9	to	to	ADP
fcis-22002	68	10	-	-	PUNCT
fcis-22002	68	11	end	end	NOUN
fcis-22002	68	12	speech	speech	NOUN
fcis-22002	68	13	retrieval	retrieval	NOUN
fcis-22002	68	14	,	,	PUNCT
fcis-22002	68	15	it	it	PRON
fcis-22002	68	16	is	be	AUX
fcis-22002	68	17	not	not	PART
fcis-22002	68	18	without	without	ADP
fcis-22002	68	19	its	its	PRON
fcis-22002	68	20	challenges	challenge	NOUN
fcis-22002	68	21	,	,	PUNCT
fcis-22002	68	22	such	such	ADJ
fcis-22002	68	23	as	as	ADP
fcis-22002	68	24	high	high	ADJ
fcis-22002	68	25	computational	computational	ADJ
fcis-22002	68	26	complexity	complexity	NOUN
fcis-22002	68	27	,	,	PUNCT
fcis-22002	68	28	reduced	reduce	VERB
fcis-22002	68	29	efficiency	efficiency	NOUN
fcis-22002	68	30	in	in	ADP
fcis-22002	68	31	processing	process	VERB
fcis-22002	68	32	lengthy	lengthy	ADJ
fcis-22002	68	33	speech	speech	NOUN
fcis-22002	68	34	segments	segment	NOUN
fcis-22002	68	35	,	,	PUNCT
fcis-22002	68	36	and	and	CCONJ
fcis-22002	68	37	the	the	DET
fcis-22002	68	38	potential	potential	NOUN
fcis-22002	68	39	to	to	PART
fcis-22002	68	40	overlook	overlook	VERB
fcis-22002	68	41	latent	latent	ADJ
fcis-22002	68	42	linguistic	linguistic	ADJ
fcis-22002	68	43	details	detail	NOUN
fcis-22002	68	44	.	.	PUNCT
fcis-22002	69	1	with	with	ADP
fcis-22002	69	2	the	the	DET
fcis-22002	69	3	emergence	emergence	NOUN
fcis-22002	69	4	of	of	ADP
fcis-22002	69	5	deep	deep	ADJ
fcis-22002	69	6	learning	learning	NOUN
fcis-22002	69	7	technologies	technology	NOUN
fcis-22002	69	8	,	,	PUNCT
fcis-22002	69	9	researchers	researcher	NOUN
fcis-22002	69	10	have	have	AUX
fcis-22002	69	11	been	be	AUX
fcis-22002	69	12	exploring	explore	VERB
fcis-22002	69	13	avenues	avenue	NOUN
fcis-22002	69	14	to	to	ADP
fcis-22002	69	15	harness	harness	ADJ
fcis-22002	69	16	deep	deep	ADJ
fcis-22002	69	17	learning	learning	NOUN
fcis-22002	69	18	to	to	PART
fcis-22002	69	19	surmount	surmount	VERB
fcis-22002	69	20	the	the	DET
fcis-22002	69	21	limitations	limitation	NOUN
fcis-22002	69	22	of	of	ADP
fcis-22002	69	23	these	these	DET
fcis-22002	69	24	conventional	conventional	ADJ
fcis-22002	69	25	methods	method	NOUN
fcis-22002	69	26	.	.	PUNCT
fcis-22002	70	1	acoustic	acoustic	ADJ
fcis-22002	70	2	word	word	NOUN
fcis-22002	70	3	awes	awe	VERB
fcis-22002	70	4	,	,	PUNCT
fcis-22002	70	5	for	for	ADP
fcis-22002	70	6	instance	instance	NOUN
fcis-22002	70	7	,	,	PUNCT
fcis-22002	70	8	represent	represent	VERB
fcis-22002	70	9	an	an	DET
fcis-22002	70	10	application	application	NOUN
fcis-22002	70	11	of	of	ADP
fcis-22002	70	12	deep	deep	ADJ
fcis-22002	70	13	learning	learning	NOUN
fcis-22002	70	14	in	in	ADP
fcis-22002	70	15	speech	speech	NOUN
fcis-22002	70	16	retrieval	retrieval	NOUN
fcis-22002	70	17	.	.	PUNCT
fcis-22002	71	1	the	the	DET
fcis-22002	71	2	modus	modus	ADJ
fcis-22002	71	3	operandi	operandi	NOUN
fcis-22002	71	4	of	of	ADP
fcis-22002	71	5	awes	awe	NOUN
fcis-22002	71	6	is	be	AUX
fcis-22002	71	7	to	to	PART
fcis-22002	71	8	employ	employ	VERB
fcis-22002	71	9	deep	deep	ADJ
fcis-22002	71	10	neural	neural	ADJ
fcis-22002	71	11	networks	network	NOUN
fcis-22002	71	12	to	to	PART
fcis-22002	71	13	learn	learn	VERB
fcis-22002	71	14	representations	representation	NOUN
fcis-22002	71	15	of	of	ADP
fcis-22002	71	16	speech	speech	NOUN
fcis-22002	71	17	segments	segment	NOUN
fcis-22002	71	18	.	.	PUNCT
fcis-22002	72	1	these	these	DET
fcis-22002	72	2	networks	network	NOUN
fcis-22002	72	3	are	be	AUX
fcis-22002	72	4	trained	train	VERB
fcis-22002	72	5	to	to	PART
fcis-22002	72	6	extract	extract	VERB
fcis-22002	72	7	meaningful	meaningful	ADJ
fcis-22002	72	8	features	feature	NOUN
fcis-22002	72	9	from	from	ADP
fcis-22002	72	10	the	the	DET
fcis-22002	72	11	raw	raw	ADJ
fcis-22002	72	12	speech	speech	NOUN
fcis-22002	72	13	signal	signal	NOUN
fcis-22002	72	14	and	and	CCONJ
fcis-22002	72	15	map	map	VERB
fcis-22002	72	16	these	these	DET
fcis-22002	72	17	features	feature	NOUN
fcis-22002	72	18	to	to	ADP
fcis-22002	72	19	a	a	DET
fcis-22002	72	20	low	low	ADJ
fcis-22002	72	21	-	-	PUNCT
fcis-22002	72	22	dimensional	dimensional	ADJ
fcis-22002	72	23	space	space	NOUN
fcis-22002	72	24	,	,	PUNCT
fcis-22002	72	25	typically	typically	ADV
fcis-22002	72	26	a	a	DET
fcis-22002	72	27	fixed	fix	VERB
fcis-22002	72	28	-	-	PUNCT
fcis-22002	72	29	length	length	NOUN
fcis-22002	72	30	vector	vector	NOUN
fcis-22002	72	31	.	.	PUNCT
fcis-22002	73	1	these	these	DET
fcis-22002	73	2	vectors	vector	NOUN
fcis-22002	73	3	,	,	PUNCT
fcis-22002	73	4	or	or	CCONJ
fcis-22002	73	5	”	"	PUNCT
fcis-22002	73	6	embeddings	embedding	NOUN
fcis-22002	73	7	,	,	PUNCT
fcis-22002	73	8	”	"	PUNCT
fcis-22002	73	9	serve	serve	VERB
fcis-22002	73	10	as	as	ADP
fcis-22002	73	11	a	a	DET
fcis-22002	73	12	compact	compact	ADJ
fcis-22002	73	13	representation	representation	NOUN
fcis-22002	73	14	of	of	ADP
fcis-22002	73	15	speech	speech	NOUN
fcis-22002	73	16	segments	segment	NOUN
fcis-22002	73	17	,	,	PUNCT
fcis-22002	73	18	retaining	retain	VERB
fcis-22002	73	19	crucial	crucial	ADJ
fcis-22002	73	20	information	information	NOUN
fcis-22002	73	21	from	from	ADP
fcis-22002	73	22	the	the	DET
fcis-22002	73	23	original	original	ADJ
fcis-22002	73	24	speech	speech	NOUN
fcis-22002	73	25	while	while	SCONJ
fcis-22002	73	26	eliminating	eliminate	VERB
fcis-22002	73	27	irrelevant	irrelevant	ADJ
fcis-22002	73	28	or	or	CCONJ
fcis-22002	73	29	redundant	redundant	ADJ
fcis-22002	73	30	content	content	NOUN
fcis-22002	73	31	.	.	PUNCT
fcis-22002	74	1	as	as	SCONJ
fcis-22002	74	2	demonstrated	demonstrate	VERB
fcis-22002	74	3	in	in	ADP
fcis-22002	74	4	the	the	DET
fcis-22002	74	5	research	research	NOUN
fcis-22002	74	6	by	by	ADP
fcis-22002	74	7	shen	shen	PROPN
fcis-22002	74	8	et	et	PROPN
fcis-22002	74	9	al.[18	al.[18	PROPN
fcis-22002	74	10	]	]	PUNCT
fcis-22002	74	11	,	,	PUNCT
fcis-22002	74	12	they	they	PRON
fcis-22002	74	13	proposed	propose	VERB
fcis-22002	74	14	a	a	DET
fcis-22002	74	15	novel	novel	ADJ
fcis-22002	74	16	and	and	CCONJ
fcis-22002	74	17	effective	effective	ADJ
fcis-22002	74	18	approach	approach	NOUN
fcis-22002	74	19	by	by	ADP
fcis-22002	74	20	jointly	jointly	ADV
fcis-22002	74	21	training	train	VERB
fcis-22002	74	22	acoustic	acoustic	ADJ
fcis-22002	74	23	phoneme	phoneme	NOUN
fcis-22002	74	24	and	and	CCONJ
fcis-22002	74	25	word	word	NOUN
fcis-22002	74	26	embed	embed	NOUN
fcis-22002	74	27	dings	ding	NOUN
fcis-22002	74	28	for	for	ADP
fcis-22002	74	29	end	end	NOUN
fcis-22002	74	30	-	-	PUNCT
fcis-22002	74	31	to	to	ADP
fcis-22002	74	32	-	-	PUNCT
fcis-22002	74	33	end	end	NOUN
fcis-22002	74	34	text	text	NOUN
fcis-22002	74	35	-	-	PUNCT
fcis-22002	74	36	tospeech(tts	tospeech(tts	NOUN
fcis-22002	74	37	)	)	PUNCT
fcis-22002	74	38	systems	system	NOUN
fcis-22002	74	39	.	.	PUNCT
fcis-22002	75	1	contrasting	contrast	VERB
fcis-22002	75	2	with	with	ADP
fcis-22002	75	3	awes	awe	NOUN
fcis-22002	75	4	,	,	PUNCT
fcis-22002	75	5	which	which	PRON
fcis-22002	75	6	primarily	primarily	ADV
fcis-22002	75	7	focuses	focus	VERB
fcis-22002	75	8	on	on	ADP
fcis-22002	75	9	keywords	keyword	NOUN
fcis-22002	75	10	,	,	PUNCT
fcis-22002	75	11	our	our	PRON
fcis-22002	75	12	method	method	NOUN
fcis-22002	75	13	in	in	ADP
fcis-22002	75	14	this	this	DET
fcis-22002	75	15	paper	paper	NOUN
fcis-22002	75	16	accentuates	accentuate	VERB
fcis-22002	75	17	the	the	DET
fcis-22002	75	18	comparison	comparison	NOUN
fcis-22002	75	19	of	of	ADP
fcis-22002	75	20	entire	entire	ADJ
fcis-22002	75	21	sentences	sentence	NOUN
fcis-22002	75	22	,	,	PUNCT
fcis-22002	75	23	achieving	achieve	VERB
fcis-22002	75	24	qbe	qbe	PROPN
fcis-22002	75	25	speech	speech	NOUN
fcis-22002	75	26	retrieval	retrieval	NOUN
fcis-22002	75	27	with	with	ADP
fcis-22002	75	28	a	a	DET
fcis-22002	75	29	more	more	ADV
fcis-22002	75	30	comprehensive	comprehensive	ADJ
fcis-22002	75	31	content	content	NOUN
fcis-22002	75	32	match	match	VERB
fcis-22002	75	33	2.3	2.3	NUM
fcis-22002	75	34	.	.	PUNCT
fcis-22002	76	1	causal	causal	ADJ
fcis-22002	76	2	dilated	dilate	VERB
fcis-22002	76	3	convolution	convolution	NOUN
fcis-22002	76	4	and	and	CCONJ
fcis-22002	76	5	wavenet	wavenet	PROPN
fcis-22002	76	6	wavenet	wavenet	PROPN
fcis-22002	76	7	is	be	AUX
fcis-22002	76	8	a	a	DET
fcis-22002	76	9	speech	speech	NOUN
fcis-22002	76	10	generation	generation	NOUN
fcis-22002	76	11	model	model	NOUN
fcis-22002	76	12	proposed	propose	VERB
fcis-22002	76	13	by	by	ADP
fcis-22002	76	14	deepmind	deepmind	NOUN
fcis-22002	76	15	in	in	ADP
fcis-22002	76	16	2016	2016	NUM
fcis-22002	76	17	,	,	PUNCT
fcis-22002	76	18	which	which	PRON
fcis-22002	76	19	is	be	AUX
fcis-22002	76	20	able	able	ADJ
fcis-22002	76	21	to	to	PART
fcis-22002	76	22	model	model	VERB
fcis-22002	76	23	the	the	DET
fcis-22002	76	24	raw	raw	ADJ
fcis-22002	76	25	speech	speech	NOUN
fcis-22002	76	26	data	datum	NOUN
fcis-22002	76	27	directly	directly	ADV
fcis-22002	76	28	,	,	PUNCT
fcis-22002	76	29	with	with	ADP
fcis-22002	76	30	the	the	DET
fcis-22002	76	31	predicted	predict	VERB
fcis-22002	76	32	distribution	distribution	NOUN
fcis-22002	76	33	of	of	ADP
fcis-22002	76	34	each	each	DET
fcis-22002	76	35	audio	audio	ADJ
fcis-22002	76	36	sample	sample	NOUN
fcis-22002	76	37	conditional	conditional	ADJ
fcis-22002	76	38	on	on	ADP
fcis-22002	76	39	all	all	DET
fcis-22002	76	40	previous	previous	ADJ
fcis-22002	76	41	samples[19	samples[19	NOUN
fcis-22002	76	42	]	]	PUNCT
fcis-22002	76	43	.	.	PUNCT
fcis-22002	77	1	it	it	PRON
fcis-22002	77	2	can	can	AUX
fcis-22002	77	3	generate	generate	VERB
fcis-22002	77	4	natural	natural	ADJ
fcis-22002	77	5	speech	speech	NOUN
fcis-22002	77	6	signals	signal	NOUN
fcis-22002	77	7	on	on	ADP
fcis-22002	77	8	text	text	NOUN
fcis-22002	77	9	-	-	PUNCT
fcis-22002	77	10	to	to	ADP
fcis-22002	77	11	-	-	PUNCT
fcis-22002	77	12	speech	speech	NOUN
fcis-22002	77	13	tasks	task	NOUN
fcis-22002	77	14	.	.	PUNCT
fcis-22002	78	1	the	the	DET
fcis-22002	78	2	concepts	concept	NOUN
fcis-22002	78	3	of	of	ADP
fcis-22002	78	4	causal	causal	ADJ
fcis-22002	78	5	convolution	convolution	NOUN
fcis-22002	78	6	and	and	CCONJ
fcis-22002	78	7	dilated	dilated	ADJ
fcis-22002	78	8	convolution	convolution	NOUN
fcis-22002	78	9	are	be	AUX
fcis-22002	78	10	proposed	propose	VERB
fcis-22002	78	11	in	in	ADP
fcis-22002	78	12	wavenet	wavenet	NOUN
fcis-22002	78	13	.	.	PUNCT
fcis-22002	79	1	causal	causal	ADJ
fcis-22002	79	2	convolution	convolution	NOUN
fcis-22002	79	3	does	do	AUX
fcis-22002	79	4	not	not	PART
fcis-22002	79	5	depend	depend	VERB
fcis-22002	79	6	on	on	ADP
fcis-22002	79	7	any	any	DET
fcis-22002	79	8	future	future	ADJ
fcis-22002	79	9	time	time	NOUN
fcis-22002	79	10	step	step	NOUN
fcis-22002	79	11	when	when	SCONJ
fcis-22002	79	12	processing	process	VERB
fcis-22002	79	13	the	the	DET
fcis-22002	79	14	current	current	ADJ
fcis-22002	79	15	time	time	NOUN
fcis-22002	79	16	step[20	step[20	PROPN
fcis-22002	79	17	]	]	PUNCT
fcis-22002	79	18	.	.	PUNCT
fcis-22002	80	1	since	since	SCONJ
fcis-22002	80	2	models	model	NOUN
fcis-22002	80	3	with	with	ADP
fcis-22002	80	4	causal	causal	ADJ
fcis-22002	80	5	convolution	convolution	NOUN
fcis-22002	80	6	do	do	AUX
fcis-22002	80	7	not	not	PART
fcis-22002	80	8	have	have	VERB
fcis-22002	80	9	recursive	recursive	ADJ
fcis-22002	80	10	connections	connection	NOUN
fcis-22002	80	11	,	,	PUNCT
fcis-22002	80	12	they	they	PRON
fcis-22002	80	13	are	be	AUX
fcis-22002	80	14	usually	usually	ADV
fcis-22002	80	15	trained	train	VERB
fcis-22002	80	16	faster	fast	ADV
fcis-22002	80	17	than	than	ADP
fcis-22002	80	18	recurrent	recurrent	ADJ
fcis-22002	80	19	neural	neural	ADJ
fcis-22002	80	20	networks	network	NOUN
fcis-22002	80	21	(	(	PUNCT
fcis-22002	80	22	rnns	rnns	PROPN
fcis-22002	80	23	)	)	PUNCT
fcis-22002	80	24	,	,	PUNCT
fcis-22002	80	25	and	and	CCONJ
fcis-22002	80	26	their	their	PRON
fcis-22002	80	27	fast	fast	ADJ
fcis-22002	80	28	training	training	NOUN
fcis-22002	80	29	speed	speed	NOUN
fcis-22002	80	30	becomes	become	VERB
fcis-22002	80	31	more	more	ADV
fcis-22002	80	32	pronounced	pronounced	ADJ
fcis-22002	80	33	when	when	SCONJ
fcis-22002	80	34	the	the	DET
fcis-22002	80	35	sequence	sequence	NOUN
fcis-22002	80	36	length	length	NOUN
fcis-22002	80	37	is	be	AUX
fcis-22002	80	38	longer	long	ADV
fcis-22002	80	39	.	.	PUNCT
fcis-22002	81	1	dilated	dilate	VERB
fcis-22002	81	2	convolution	convolution	NOUN
fcis-22002	81	3	improves	improve	VERB
fcis-22002	81	4	the	the	DET
fcis-22002	81	5	efficiency	efficiency	NOUN
fcis-22002	81	6	of	of	ADP
fcis-22002	81	7	applying	apply	VERB
fcis-22002	81	8	filters	filter	NOUN
fcis-22002	81	9	over	over	ADP
fcis-22002	81	10	large	large	ADJ
fcis-22002	81	11	regions	region	NOUN
fcis-22002	81	12	by	by	ADP
fcis-22002	81	13	skipping	skip	VERB
fcis-22002	81	14	parts	part	NOUN
fcis-22002	81	15	of	of	ADP
fcis-22002	81	16	the	the	DET
fcis-22002	81	17	input	input	NOUN
fcis-22002	81	18	data	datum	NOUN
fcis-22002	81	19	in	in	ADP
fcis-22002	81	20	certain	certain	ADJ
fcis-22002	81	21	steps	step	NOUN
fcis-22002	81	22	.	.	PUNCT
fcis-22002	82	1	although	although	SCONJ
fcis-22002	82	2	wavenet	wavenet	NOUN
fcis-22002	82	3	is	be	AUX
fcis-22002	82	4	designed	design	VERB
fcis-22002	82	5	as	as	ADP
fcis-22002	82	6	a	a	DET
fcis-22002	82	7	generative	generative	ADJ
fcis-22002	82	8	model	model	NOUN
fcis-22002	82	9	,	,	PUNCT
fcis-22002	82	10	it	it	PRON
fcis-22002	82	11	can	can	AUX
fcis-22002	82	12	be	be	AUX
fcis-22002	82	13	directly	directly	ADV
fcis-22002	82	14	applied	apply	VERB
fcis-22002	82	15	to	to	ADP
fcis-22002	82	16	speech	speech	NOUN
fcis-22002	82	17	recognition	recognition	NOUN
fcis-22002	82	18	tasks	task	NOUN
fcis-22002	82	19	.	.	PUNCT
fcis-22002	83	1	gao	gao	PROPN
fcis-22002	83	2	et	et	PROPN
fcis-22002	83	3	al	al	PROPN
fcis-22002	83	4	.	.	PROPN
fcis-22002	83	5	presented	present	VERB
fcis-22002	83	6	the	the	DET
fcis-22002	83	7	addition	addition	NOUN
fcis-22002	83	8	of	of	ADP
fcis-22002	83	9	local	local	ADJ
fcis-22002	83	10	attention	attention	NOUN
fcis-22002	83	11	to	to	ADP
fcis-22002	83	12	wavenet	wavenet	NOUN
fcis-22002	83	13	-	-	PUNCT
fcis-22002	83	14	ctc	ctc	PROPN
fcis-22002	83	15	to	to	PART
fcis-22002	83	16	increase	increase	VERB
fcis-22002	83	17	the	the	DET
fcis-22002	83	18	performance	performance	NOUN
fcis-22002	83	19	of	of	ADP
fcis-22002	83	20	tibetan	tibetan	ADJ
fcis-22002	83	21	language	language	PROPN
fcis-22002	83	22	recognitionin	recognitionin	ADJ
fcis-22002	83	23	multi	multi	NOUN
fcis-22002	83	24	-	-	NOUN
fcis-22002	83	25	task	task	NOUN
fcis-22002	83	26	learning.[21	learning.[21	PROPN
fcis-22002	83	27	]	]	PUNCT
fcis-22002	83	28	.	.	PUNCT
fcis-22002	84	1	2.4	2.4	NUM
fcis-22002	84	2	.	.	PUNCT
fcis-22002	85	1	multihead	multihead	NOUN
fcis-22002	85	2	self	self	NOUN
fcis-22002	85	3	-	-	PUNCT
fcis-22002	85	4	attention	attention	NOUN
fcis-22002	85	5	mechanism	mechanism	NOUN
fcis-22002	85	6	and	and	CCONJ
fcis-22002	85	7	transformer	transformer	NOUN
fcis-22002	85	8	transformer	transformer	NOUN
fcis-22002	85	9	,	,	PUNCT
fcis-22002	85	10	an	an	DET
fcis-22002	85	11	e2e	e2e	PROPN
fcis-22002	85	12	model	model	NOUN
fcis-22002	85	13	proposed	propose	VERB
fcis-22002	85	14	by	by	ADP
fcis-22002	85	15	google	google	PROPN
fcis-22002	85	16	in	in	ADP
fcis-22002	85	17	2017	2017	NUM
fcis-22002	85	18	,	,	PUNCT
fcis-22002	85	19	is	be	AUX
fcis-22002	85	20	a	a	DET
fcis-22002	85	21	model	model	NOUN
fcis-22002	85	22	in	in	ADP
fcis-22002	85	23	which	which	PRON
fcis-22002	85	24	both	both	CCONJ
fcis-22002	85	25	encoders	encoder	NOUN
fcis-22002	85	26	and	and	CCONJ
fcis-22002	85	27	decoders	decoder	NOUN
fcis-22002	85	28	rely	rely	VERB
fcis-22002	85	29	on	on	ADP
fcis-22002	85	30	selfattention	selfattention	NOUN
fcis-22002	85	31	to	to	PART
fcis-22002	85	32	compute	compute	VERB
fcis-22002	85	33	their	their	PRON
fcis-22002	85	34	inputs	input	NOUN
fcis-22002	85	35	and	and	CCONJ
fcis-22002	85	36	outputs	output	NOUN
fcis-22002	85	37	,	,	PUNCT
fcis-22002	85	38	and	and	CCONJ
fcis-22002	85	39	it	it	PRON
fcis-22002	85	40	is	be	AUX
fcis-22002	85	41	the	the	DET
fcis-22002	85	42	first	first	ADJ
fcis-22002	85	43	transduction	transduction	NOUN
fcis-22002	85	44	model	model	NOUN
fcis-22002	85	45	that	that	PRON
fcis-22002	85	46	does	do	AUX
fcis-22002	85	47	not	not	PART
fcis-22002	85	48	use	use	VERB
fcis-22002	85	49	sequence	sequence	NOUN
fcis-22002	85	50	-	-	PUNCT
fcis-22002	85	51	aligned	align	VERB
fcis-22002	85	52	rnns	rnn	NOUN
fcis-22002	85	53	or	or	CCONJ
fcis-22002	85	54	cnns	cnn	NOUN
fcis-22002	85	55	,	,	PUNCT
fcis-22002	85	56	which	which	PRON
fcis-22002	85	57	have	have	AUX
fcis-22002	85	58	received	receive	VERB
fcis-22002	85	59	a	a	DET
fcis-22002	85	60	lot	lot	NOUN
fcis-22002	85	61	of	of	ADP
fcis-22002	85	62	attention	attention	NOUN
fcis-22002	85	63	for	for	ADP
fcis-22002	85	64	their	their	PRON
fcis-22002	85	65	effectiveness	effectiveness	NOUN
fcis-22002	85	66	in	in	ADP
fcis-22002	85	67	areas	area	NOUN
fcis-22002	85	68	such	such	ADJ
fcis-22002	85	69	as	as	ADP
fcis-22002	85	70	computer	computer	NOUN
fcis-22002	85	71	vision	vision	NOUN
fcis-22002	85	72	and	and	CCONJ
fcis-22002	85	73	natural	natural	ADJ
fcis-22002	85	74	language	language	NOUN
fcis-22002	85	75	processing	processing	NOUN
fcis-22002	85	76	.	.	PUNCT
fcis-22002	86	1	a	a	DET
fcis-22002	86	2	transformer	transformer	NOUN
fcis-22002	86	3	is	be	AUX
fcis-22002	86	4	a	a	DET
fcis-22002	86	5	multilayer	multilayer	ADJ
fcis-22002	86	6	architecture	architecture	NOUN
fcis-22002	86	7	formed	form	VERB
fcis-22002	86	8	by	by	ADP
fcis-22002	86	9	transformer	transformer	NOUN
fcis-22002	86	10	blocks	block	NOUN
fcis-22002	86	11	stacked	stack	VERB
fcis-22002	86	12	together	together	ADV
fcis-22002	86	13	.	.	PUNCT
fcis-22002	87	1	the	the	DET
fcis-22002	87	2	transformer	transformer	NOUN
fcis-22002	87	3	block	block	NOUN
fcis-22002	87	4	consists	consist	VERB
fcis-22002	87	5	of	of	ADP
fcis-22002	87	6	a	a	DET
fcis-22002	87	7	multi	multi	ADJ
fcis-22002	87	8	-	-	ADJ
fcis-22002	87	9	headed	headed	ADJ
fcis-22002	87	10	selfattentive	selfattentive	ADJ
fcis-22002	87	11	mechanism	mechanism	NOUN
fcis-22002	87	12	,	,	PUNCT
fcis-22002	87	13	a	a	DET
fcis-22002	87	14	position	position	NOUN
fcis-22002	87	15	feedforward	feedforward	NOUN
fcis-22002	87	16	network	network	NOUN
fcis-22002	87	17	,	,	PUNCT
fcis-22002	87	18	a	a	DET
fcis-22002	87	19	layer	layer	NOUN
fcis-22002	87	20	24	24	NUM
fcis-22002	87	21	normalization	normalization	NOUN
fcis-22002	87	22	module	module	NOUN
fcis-22002	87	23	,	,	PUNCT
fcis-22002	87	24	and	and	CCONJ
fcis-22002	87	25	a	a	DET
fcis-22002	87	26	residual	residual	ADJ
fcis-22002	87	27	joint	joint	NOUN
fcis-22002	87	28	.	.	PUNCT
fcis-22002	88	1	the	the	DET
fcis-22002	88	2	self	self	NOUN
fcis-22002	88	3	-	-	PUNCT
fcis-22002	88	4	attention	attention	NOUN
fcis-22002	88	5	mechanism	mechanism	NOUN
fcis-22002	88	6	is	be	AUX
fcis-22002	88	7	the	the	DET
fcis-22002	88	8	key	key	ADJ
fcis-22002	88	9	concept	concept	NOUN
fcis-22002	88	10	of	of	ADP
fcis-22002	88	11	transformer	transformer	NOUN
fcis-22002	88	12	.	.	PUNCT
fcis-22002	89	1	compared	compare	VERB
fcis-22002	89	2	with	with	ADP
fcis-22002	89	3	rnn	rnn	NOUN
fcis-22002	89	4	and	and	CCONJ
fcis-22002	89	5	lstm	lstm	PROPN
fcis-22002	89	6	,	,	PUNCT
fcis-22002	89	7	the	the	DET
fcis-22002	89	8	selfattentive	selfattentive	ADJ
fcis-22002	89	9	mechanism	mechanism	NOUN
fcis-22002	89	10	is	be	AUX
fcis-22002	89	11	more	more	ADV
fcis-22002	89	12	likely	likely	ADJ
fcis-22002	89	13	to	to	PART
fcis-22002	89	14	capture	capture	VERB
fcis-22002	89	15	long	long	ADJ
fcis-22002	89	16	-	-	PUNCT
fcis-22002	89	17	distance	distance	NOUN
fcis-22002	89	18	interdependent	interdependent	ADJ
fcis-22002	89	19	features	feature	NOUN
fcis-22002	89	20	in	in	ADP
fcis-22002	89	21	a	a	DET
fcis-22002	89	22	sequence	sequence	NOUN
fcis-22002	89	23	.	.	PUNCT
fcis-22002	90	1	it	it	PRON
fcis-22002	90	2	represents	represent	VERB
fcis-22002	90	3	the	the	DET
fcis-22002	90	4	connection	connection	NOUN
fcis-22002	90	5	between	between	ADP
fcis-22002	90	6	any	any	DET
fcis-22002	90	7	two	two	NUM
fcis-22002	90	8	steps	step	NOUN
fcis-22002	90	9	in	in	ADP
fcis-22002	90	10	the	the	DET
fcis-22002	90	11	sequence	sequence	NOUN
fcis-22002	90	12	directly	directly	ADV
fcis-22002	90	13	by	by	ADP
fcis-22002	90	14	a	a	DET
fcis-22002	90	15	computational	computational	ADJ
fcis-22002	90	16	result	result	NOUN
fcis-22002	90	17	,	,	PUNCT
fcis-22002	90	18	and	and	CCONJ
fcis-22002	90	19	the	the	DET
fcis-22002	90	20	distance	distance	NOUN
fcis-22002	90	21	between	between	ADP
fcis-22002	90	22	distantdependent	distantdependent	ADJ
fcis-22002	90	23	features	feature	NOUN
fcis-22002	90	24	is	be	AUX
fcis-22002	90	25	greatly	greatly	ADV
fcis-22002	90	26	reduced	reduce	VERB
fcis-22002	90	27	,	,	PUNCT
fcis-22002	90	28	which	which	PRON
fcis-22002	90	29	facilitates	facilitate	VERB
fcis-22002	90	30	the	the	DET
fcis-22002	90	31	effective	effective	ADJ
fcis-22002	90	32	use	use	NOUN
fcis-22002	90	33	of	of	ADP
fcis-22002	90	34	these	these	DET
fcis-22002	90	35	features	feature	NOUN
fcis-22002	90	36	.	.	PUNCT
fcis-22002	91	1	the	the	DET
fcis-22002	91	2	multi	multi	ADJ
fcis-22002	91	3	-	-	ADJ
fcis-22002	91	4	headed	headed	ADJ
fcis-22002	91	5	attention	attention	NOUN
fcis-22002	91	6	mechanism	mechanism	NOUN
fcis-22002	91	7	consists	consist	VERB
fcis-22002	91	8	of	of	ADP
fcis-22002	91	9	multiple	multiple	ADJ
fcis-22002	91	10	self	self	NOUN
fcis-22002	91	11	-	-	PUNCT
fcis-22002	91	12	attentions	attention	NOUN
fcis-22002	91	13	that	that	PRON
fcis-22002	91	14	can	can	AUX
fcis-22002	91	15	simultaneously	simultaneously	ADV
fcis-22002	91	16	attend	attend	VERB
fcis-22002	91	17	to	to	ADP
fcis-22002	91	18	information	information	NOUN
fcis-22002	91	19	from	from	ADP
fcis-22002	91	20	different	different	ADJ
fcis-22002	91	21	representation	representation	NOUN
fcis-22002	91	22	subspaces	subspace	NOUN
fcis-22002	91	23	at	at	ADP
fcis-22002	91	24	different	different	ADJ
fcis-22002	91	25	locations	location	NOUN
fcis-22002	91	26	.	.	PUNCT
fcis-22002	92	1	transformer	transformer	NOUN
fcis-22002	92	2	and	and	CCONJ
fcis-22002	92	3	its	its	PRON
fcis-22002	92	4	derived	derived	ADJ
fcis-22002	92	5	models	model	NOUN
fcis-22002	92	6	have	have	AUX
fcis-22002	92	7	been	be	AUX
fcis-22002	92	8	widely	widely	ADV
fcis-22002	92	9	used	use	VERB
fcis-22002	92	10	in	in	ADP
fcis-22002	92	11	tasks	task	NOUN
fcis-22002	92	12	such	such	ADJ
fcis-22002	92	13	as	as	ADP
fcis-22002	92	14	audio	audio	ADJ
fcis-22002	92	15	classification	classification	NOUN
fcis-22002	92	16	[	[	X
fcis-22002	92	17	22	22	NUM
fcis-22002	92	18	]	]	PUNCT
fcis-22002	92	19	,	,	PUNCT
fcis-22002	92	20	sentiment	sentiment	NOUN
fcis-22002	92	21	analysis	analysis	NOUN
fcis-22002	92	22	[	[	X
fcis-22002	92	23	23	23	NUM
fcis-22002	92	24	]	]	PUNCT
fcis-22002	92	25	,	,	PUNCT
fcis-22002	92	26	etc	etc	X
fcis-22002	92	27	.	.	X
fcis-22002	92	28	organization	organization	NOUN
fcis-22002	92	29	of	of	ADP
fcis-22002	92	30	the	the	DET
fcis-22002	92	31	text	text	NOUN
fcis-22002	92	32	3	3	X
fcis-22002	92	33	.	.	PUNCT
fcis-22002	93	1	the	the	DET
fcis-22002	93	2	method	method	NOUN
fcis-22002	93	3	3.1	3.1	NUM
fcis-22002	93	4	.	.	PUNCT
fcis-22002	93	5	model	model	NOUN
fcis-22002	93	6	in	in	ADP
fcis-22002	93	7	order	order	NOUN
fcis-22002	93	8	to	to	PART
fcis-22002	93	9	realize	realize	VERB
fcis-22002	93	10	speech	speech	NOUN
fcis-22002	93	11	retrieval	retrieval	NOUN
fcis-22002	93	12	through	through	ADP
fcis-22002	93	13	speech	speech	NOUN
fcis-22002	93	14	content	content	NOUN
fcis-22002	93	15	,	,	PUNCT
fcis-22002	93	16	we	we	PRON
fcis-22002	93	17	design	design	VERB
fcis-22002	93	18	a	a	DET
fcis-22002	93	19	hybrid	hybrid	ADJ
fcis-22002	93	20	model	model	NOUN
fcis-22002	93	21	.	.	PUNCT
fcis-22002	94	1	the	the	DET
fcis-22002	94	2	model	model	NOUN
fcis-22002	94	3	maps	map	VERB
fcis-22002	94	4	the	the	DET
fcis-22002	94	5	distances	distance	NOUN
fcis-22002	94	6	between	between	ADP
fcis-22002	94	7	the	the	DET
fcis-22002	94	8	contents	content	NOUN
fcis-22002	94	9	to	to	ADP
fcis-22002	94	10	the	the	DET
fcis-22002	94	11	hamming	hamming	NOUN
fcis-22002	94	12	space	space	NOUN
fcis-22002	94	13	,	,	PUNCT
fcis-22002	94	14	generates	generate	VERB
fcis-22002	94	15	binary	binary	ADJ
fcis-22002	94	16	hash	hash	NOUN
fcis-22002	94	17	codes	code	NOUN
fcis-22002	94	18	related	relate	VERB
fcis-22002	94	19	to	to	ADP
fcis-22002	94	20	the	the	DET
fcis-22002	94	21	contents	content	NOUN
fcis-22002	94	22	,	,	PUNCT
fcis-22002	94	23	and	and	CCONJ
fcis-22002	94	24	completes	complete	VERB
fcis-22002	94	25	the	the	DET
fcis-22002	94	26	matching	matching	NOUN
fcis-22002	94	27	by	by	ADP
fcis-22002	94	28	comparing	compare	VERB
fcis-22002	94	29	the	the	DET
fcis-22002	94	30	hamming	hamming	ADJ
fcis-22002	94	31	distances	distance	NOUN
fcis-22002	94	32	between	between	ADP
fcis-22002	94	33	the	the	DET
fcis-22002	94	34	hash	hash	NOUN
fcis-22002	94	35	codes	code	NOUN
fcis-22002	94	36	.	.	PUNCT
fcis-22002	95	1	the	the	DET
fcis-22002	95	2	model	model	NOUN
fcis-22002	95	3	consists	consist	VERB
fcis-22002	95	4	of	of	ADP
fcis-22002	95	5	three	three	NUM
fcis-22002	95	6	main	main	ADJ
fcis-22002	95	7	parts	part	NOUN
fcis-22002	95	8	:	:	PUNCT
fcis-22002	95	9	a	a	DET
fcis-22002	95	10	wavenet	wavenet	NOUN
fcis-22002	95	11	-	-	PUNCT
fcis-22002	95	12	based	base	VERB
fcis-22002	95	13	encoder	encoder	NOUN
fcis-22002	95	14	,	,	PUNCT
fcis-22002	95	15	a	a	DET
fcis-22002	95	16	downsampling	downsample	VERB
fcis-22002	95	17	module	module	NOUN
fcis-22002	95	18	based	base	VERB
fcis-22002	95	19	on	on	ADP
fcis-22002	95	20	multilayer	multilayer	ADJ
fcis-22002	95	21	causal	causal	NOUN
fcis-22002	95	22	convolution	convolution	NOUN
fcis-22002	95	23	and	and	CCONJ
fcis-22002	95	24	pooling	pooling	NOUN
fcis-22002	95	25	,	,	PUNCT
fcis-22002	95	26	and	and	CCONJ
fcis-22002	95	27	a	a	DET
fcis-22002	95	28	decoder	decoder	NOUN
fcis-22002	95	29	consisting	consist	VERB
fcis-22002	95	30	of	of	ADP
fcis-22002	95	31	a	a	DET
fcis-22002	95	32	transformer	transformer	NOUN
fcis-22002	95	33	block	block	NOUN
fcis-22002	95	34	.	.	PUNCT
fcis-22002	96	1	each	each	DET
fcis-22002	96	2	module	module	NOUN
fcis-22002	96	3	is	be	AUX
fcis-22002	96	4	described	describe	VERB
fcis-22002	96	5	in	in	ADP
fcis-22002	96	6	detail	detail	NOUN
fcis-22002	96	7	below	below	ADV
fcis-22002	96	8	.	.	PUNCT
fcis-22002	97	1	pre	pre	ADJ
fcis-22002	97	2	-	-	ADJ
fcis-22002	97	3	training	training	ADJ
fcis-22002	97	4	encoder	encoder	NOUN
fcis-22002	97	5	:	:	PUNCT
fcis-22002	97	6	in	in	ADP
fcis-22002	97	7	order	order	NOUN
fcis-22002	97	8	to	to	PART
fcis-22002	97	9	extract	extract	VERB
fcis-22002	97	10	content	content	NOUN
fcis-22002	97	11	-	-	PUNCT
fcis-22002	97	12	related	relate	VERB
fcis-22002	97	13	features	feature	NOUN
fcis-22002	97	14	from	from	ADP
fcis-22002	97	15	the	the	DET
fcis-22002	97	16	speech	speech	NOUN
fcis-22002	97	17	signal	signal	NOUN
fcis-22002	97	18	,	,	PUNCT
fcis-22002	97	19	we	we	PRON
fcis-22002	97	20	employ	employ	VERB
fcis-22002	97	21	a	a	DET
fcis-22002	97	22	wavenet	wavenet	NOUN
fcis-22002	97	23	-	-	PUNCT
fcis-22002	97	24	based	base	VERB
fcis-22002	97	25	pre	pre	ADJ
fcis-22002	97	26	-	-	ADJ
fcis-22002	97	27	training	training	ADJ
fcis-22002	97	28	encoder	encoder	NOUN
fcis-22002	97	29	.	.	PUNCT
fcis-22002	98	1	the	the	DET
fcis-22002	98	2	construction	construction	NOUN
fcis-22002	98	3	of	of	ADP
fcis-22002	98	4	the	the	DET
fcis-22002	98	5	encoder	encoder	NOUN
fcis-22002	98	6	starts	start	VERB
fcis-22002	98	7	with	with	ADP
fcis-22002	98	8	the	the	DET
fcis-22002	98	9	conversion	conversion	NOUN
fcis-22002	98	10	of	of	ADP
fcis-22002	98	11	the	the	DET
fcis-22002	98	12	mfcc	mfcc	NOUN
fcis-22002	98	13	features	feature	VERB
fcis-22002	98	14	of	of	ADP
fcis-22002	98	15	the	the	DET
fcis-22002	98	16	speech	speech	NOUN
fcis-22002	98	17	into	into	ADP
fcis-22002	98	18	a	a	DET
fcis-22002	98	19	set	set	NOUN
fcis-22002	98	20	of	of	ADP
fcis-22002	98	21	initial	initial	ADJ
fcis-22002	98	22	features	feature	NOUN
fcis-22002	98	23	.	.	PUNCT
fcis-22002	99	1	this	this	DET
fcis-22002	99	2	process	process	NOUN
fcis-22002	99	3	is	be	AUX
fcis-22002	99	4	achieved	achieve	VERB
fcis-22002	99	5	by	by	ADP
fcis-22002	99	6	applying	apply	VERB
fcis-22002	99	7	a	a	DET
fcis-22002	99	8	one	one	NUM
fcis-22002	99	9	-	-	PUNCT
fcis-22002	99	10	dimensional	dimensional	ADJ
fcis-22002	99	11	convolutional	convolutional	ADJ
fcis-22002	99	12	process	process	NOUN
fcis-22002	99	13	to	to	ADP
fcis-22002	99	14	the	the	DET
fcis-22002	99	15	input	input	NOUN
fcis-22002	99	16	data	datum	NOUN
fcis-22002	99	17	,	,	PUNCT
fcis-22002	99	18	followed	follow	VERB
fcis-22002	99	19	closely	closely	ADV
fcis-22002	99	20	by	by	ADP
fcis-22002	99	21	the	the	DET
fcis-22002	99	22	batch	batch	NOUN
fcis-22002	99	23	normalization	normalization	NOUN
fcis-22002	99	24	and	and	CCONJ
fcis-22002	99	25	the	the	DET
fcis-22002	99	26	application	application	NOUN
fcis-22002	99	27	of	of	ADP
fcis-22002	99	28	an	an	DET
fcis-22002	99	29	activation	activation	NOUN
fcis-22002	99	30	function	function	NOUN
fcis-22002	99	31	to	to	PART
fcis-22002	99	32	enhance	enhance	VERB
fcis-22002	99	33	the	the	DET
fcis-22002	99	34	model	model	NOUN
fcis-22002	99	35	's	's	PART
fcis-22002	99	36	nonlinear	nonlinear	ADJ
fcis-22002	99	37	processing	processing	NOUN
fcis-22002	99	38	capability	capability	NOUN
fcis-22002	99	39	.	.	PUNCT
fcis-22002	100	1	next	next	ADV
fcis-22002	100	2	,	,	PUNCT
fcis-22002	100	3	the	the	DET
fcis-22002	100	4	encoder	encoder	NOUN
fcis-22002	100	5	introduces	introduce	VERB
fcis-22002	100	6	multiple	multiple	ADJ
fcis-22002	100	7	residual	residual	ADJ
fcis-22002	100	8	blocks	block	NOUN
fcis-22002	100	9	that	that	PRON
fcis-22002	100	10	are	be	AUX
fcis-22002	100	11	arranged	arrange	VERB
fcis-22002	100	12	repeatedly	repeatedly	ADV
fcis-22002	100	13	with	with	ADP
fcis-22002	100	14	different	different	ADJ
fcis-22002	100	15	expansion	expansion	NOUN
fcis-22002	100	16	rates	rate	NOUN
fcis-22002	100	17	to	to	PART
fcis-22002	100	18	cover	cover	VERB
fcis-22002	100	19	different	different	ADJ
fcis-22002	100	20	time	time	NOUN
fcis-22002	100	21	spans	span	NOUN
fcis-22002	100	22	.	.	PUNCT
fcis-22002	101	1	each	each	DET
fcis-22002	101	2	residual	residual	ADJ
fcis-22002	101	3	block	block	NOUN
fcis-22002	101	4	underwent	underwent	NOUN
fcis-22002	101	5	feature	feature	NOUN
fcis-22002	101	6	extraction	extraction	NOUN
fcis-22002	101	7	and	and	CCONJ
fcis-22002	101	8	generated	generate	VERB
fcis-22002	101	9	a	a	DET
fcis-22002	101	10	residual	residual	ADJ
fcis-22002	101	11	output	output	NOUN
fcis-22002	101	12	for	for	ADP
fcis-22002	101	13	subsequent	subsequent	ADJ
fcis-22002	101	14	processing	processing	NOUN
fcis-22002	101	15	.	.	PUNCT
fcis-22002	102	1	in	in	ADP
fcis-22002	102	2	this	this	DET
fcis-22002	102	3	way	way	NOUN
fcis-22002	102	4	,	,	PUNCT
fcis-22002	102	5	the	the	DET
fcis-22002	102	6	encoder	encoder	NOUN
fcis-22002	102	7	is	be	AUX
fcis-22002	102	8	able	able	ADJ
fcis-22002	102	9	to	to	PART
fcis-22002	102	10	synthesize	synthesize	VERB
fcis-22002	102	11	information	information	NOUN
fcis-22002	102	12	from	from	ADP
fcis-22002	102	13	each	each	DET
fcis-22002	102	14	time	time	NOUN
fcis-22002	102	15	step	step	NOUN
fcis-22002	102	16	,	,	PUNCT
fcis-22002	102	17	enhancing	enhance	VERB
fcis-22002	102	18	the	the	DET
fcis-22002	102	19	understanding	understanding	NOUN
fcis-22002	102	20	of	of	ADP
fcis-22002	102	21	the	the	DET
fcis-22002	102	22	speech	speech	NOUN
fcis-22002	102	23	signal	signal	NOUN
fcis-22002	102	24	.	.	PUNCT
fcis-22002	103	1	finally	finally	ADV
fcis-22002	103	2	,	,	PUNCT
fcis-22002	103	3	the	the	DET
fcis-22002	103	4	encoder	encoder	NOUN
fcis-22002	103	5	combines	combine	VERB
fcis-22002	103	6	all	all	DET
fcis-22002	103	7	residual	residual	ADJ
fcis-22002	103	8	outputs	output	NOUN
fcis-22002	103	9	and	and	CCONJ
fcis-22002	103	10	further	far	ADV
fcis-22002	103	11	processes	process	VERB
fcis-22002	103	12	them	they	PRON
fcis-22002	103	13	through	through	ADP
fcis-22002	103	14	another	another	DET
fcis-22002	103	15	layer	layer	NOUN
fcis-22002	103	16	of	of	ADP
fcis-22002	103	17	activation	activation	NOUN
fcis-22002	103	18	functions	function	NOUN
fcis-22002	103	19	to	to	PART
fcis-22002	103	20	obtain	obtain	VERB
fcis-22002	103	21	a	a	DET
fcis-22002	103	22	comprehensive	comprehensive	ADJ
fcis-22002	103	23	feature	feature	NOUN
fcis-22002	103	24	representation	representation	NOUN
fcis-22002	103	25	.	.	PUNCT
fcis-22002	104	1	during	during	ADP
fcis-22002	104	2	pre	pre	ADJ
fcis-22002	104	3	-	-	NOUN
fcis-22002	104	4	training	training	NOUN
fcis-22002	104	5	,	,	PUNCT
fcis-22002	104	6	a	a	DET
fcis-22002	104	7	layer	layer	NOUN
fcis-22002	104	8	of	of	ADP
fcis-22002	104	9	convolution	convolution	NOUN
fcis-22002	104	10	is	be	AUX
fcis-22002	104	11	added	add	VERB
fcis-22002	104	12	at	at	ADP
fcis-22002	104	13	the	the	DET
fcis-22002	104	14	end	end	NOUN
fcis-22002	104	15	of	of	ADP
fcis-22002	104	16	the	the	DET
fcis-22002	104	17	encoder	encoder	NOUN
fcis-22002	104	18	for	for	ADP
fcis-22002	104	19	mapping	map	VERB
fcis-22002	104	20	the	the	DET
fcis-22002	104	21	extracted	extract	VERB
fcis-22002	104	22	high	high	ADJ
fcis-22002	104	23	-	-	PUNCT
fcis-22002	104	24	level	level	NOUN
fcis-22002	104	25	features	feature	NOUN
fcis-22002	104	26	to	to	ADP
fcis-22002	104	27	a	a	DET
fcis-22002	104	28	specific	specific	ADJ
fcis-22002	104	29	output	output	NOUN
fcis-22002	104	30	space	space	NOUN
fcis-22002	104	31	,	,	PUNCT
fcis-22002	104	32	the	the	DET
fcis-22002	104	33	size	size	NOUN
fcis-22002	104	34	of	of	ADP
fcis-22002	104	35	which	which	PRON
fcis-22002	104	36	corresponds	correspond	VERB
fcis-22002	104	37	to	to	ADP
fcis-22002	104	38	the	the	DET
fcis-22002	104	39	size	size	NOUN
fcis-22002	104	40	of	of	ADP
fcis-22002	104	41	the	the	DET
fcis-22002	104	42	vocabulary	vocabulary	NOUN
fcis-22002	104	43	in	in	ADP
fcis-22002	104	44	the	the	DET
fcis-22002	104	45	speech	speech	NOUN
fcis-22002	104	46	recognition	recognition	NOUN
fcis-22002	104	47	task	task	NOUN
fcis-22002	104	48	.	.	PUNCT
fcis-22002	105	1	in	in	ADP
fcis-22002	105	2	short	short	ADJ
fcis-22002	105	3	,	,	PUNCT
fcis-22002	105	4	the	the	DET
fcis-22002	105	5	purpose	purpose	NOUN
fcis-22002	105	6	of	of	ADP
fcis-22002	105	7	this	this	DET
fcis-22002	105	8	layer	layer	NOUN
fcis-22002	105	9	is	be	AUX
fcis-22002	105	10	to	to	PART
fcis-22002	105	11	convert	convert	VERB
fcis-22002	105	12	the	the	DET
fcis-22002	105	13	complex	complex	ADJ
fcis-22002	105	14	speech	speech	NOUN
fcis-22002	105	15	features	feature	NOUN
fcis-22002	105	16	learned	learn	VERB
fcis-22002	105	17	by	by	ADP
fcis-22002	105	18	the	the	DET
fcis-22002	105	19	deep	deep	ADJ
fcis-22002	105	20	network	network	NOUN
fcis-22002	105	21	into	into	ADP
fcis-22002	105	22	specific	specific	ADJ
fcis-22002	105	23	vocabulary	vocabulary	ADJ
fcis-22002	105	24	predictions	prediction	NOUN
fcis-22002	105	25	.	.	PUNCT
fcis-22002	106	1	the	the	DET
fcis-22002	106	2	encoder	encoder	NOUN
fcis-22002	106	3	is	be	AUX
fcis-22002	106	4	pre	pre	VERB
fcis-22002	106	5	-	-	VERB
fcis-22002	106	6	trained	train	VERB
fcis-22002	106	7	with	with	ADP
fcis-22002	106	8	the	the	DET
fcis-22002	106	9	ctc	ctc	NOUN
fcis-22002	106	10	loss	loss	NOUN
fcis-22002	106	11	function	function	NOUN
fcis-22002	106	12	.	.	PUNCT
fcis-22002	107	1	downsampling	downsample	VERB
fcis-22002	107	2	module	module	NOUN
fcis-22002	107	3	:	:	PUNCT
fcis-22002	107	4	when	when	SCONJ
fcis-22002	107	5	processing	process	VERB
fcis-22002	107	6	acoustic	acoustic	ADJ
fcis-22002	107	7	features	feature	NOUN
fcis-22002	107	8	extracted	extract	VERB
fcis-22002	107	9	by	by	ADP
fcis-22002	107	10	wavenet	wavenet	ADJ
fcis-22002	107	11	pre	pre	ADJ
fcis-22002	107	12	-	-	ADJ
fcis-22002	107	13	trained	train	VERB
fcis-22002	107	14	encoders	encoder	NOUN
fcis-22002	107	15	,	,	PUNCT
fcis-22002	107	16	we	we	PRON
fcis-22002	107	17	noticed	notice	VERB
fcis-22002	107	18	that	that	SCONJ
fcis-22002	107	19	the	the	DET
fcis-22002	107	20	contents	content	NOUN
fcis-22002	107	21	in	in	ADP
fcis-22002	107	22	neighboring	neighboring	NOUN
fcis-22002	107	23	time	time	NOUN
fcis-22002	107	24	steps	step	NOUN
fcis-22002	107	25	are	be	AUX
fcis-22002	107	26	often	often	ADV
fcis-22002	107	27	highly	highly	ADV
fcis-22002	107	28	similar	similar	ADJ
fcis-22002	107	29	or	or	CCONJ
fcis-22002	107	30	even	even	ADV
fcis-22002	107	31	identical	identical	ADJ
fcis-22002	107	32	,	,	PUNCT
fcis-22002	107	33	which	which	PRON
fcis-22002	107	34	leads	lead	VERB
fcis-22002	107	35	to	to	ADP
fcis-22002	107	36	data	datum	NOUN
fcis-22002	107	37	redundancy	redundancy	NOUN
fcis-22002	107	38	.	.	PUNCT
fcis-22002	108	1	in	in	ADP
fcis-22002	108	2	order	order	NOUN
fcis-22002	108	3	to	to	PART
fcis-22002	108	4	deal	deal	VERB
fcis-22002	108	5	with	with	ADP
fcis-22002	108	6	this	this	DET
fcis-22002	108	7	problem	problem	NOUN
fcis-22002	108	8	effectively	effectively	ADV
fcis-22002	108	9	and	and	CCONJ
fcis-22002	108	10	reduce	reduce	VERB
fcis-22002	108	11	the	the	DET
fcis-22002	108	12	computational	computational	ADJ
fcis-22002	108	13	burden	burden	NOUN
fcis-22002	108	14	in	in	ADP
fcis-22002	108	15	subsequent	subsequent	ADJ
fcis-22002	108	16	processing	processing	NOUN
fcis-22002	108	17	steps	step	NOUN
fcis-22002	108	18	,	,	PUNCT
fcis-22002	108	19	this	this	DET
fcis-22002	108	20	study	study	NOUN
fcis-22002	108	21	introduces	introduce	VERB
fcis-22002	108	22	causal	causal	ADJ
fcis-22002	108	23	convolution	convolution	NOUN
fcis-22002	108	24	and	and	CCONJ
fcis-22002	108	25	pooling	pool	VERB
fcis-22002	108	26	layers	layer	NOUN
fcis-22002	108	27	for	for	ADP
fcis-22002	108	28	data	datum	NOUN
fcis-22002	108	29	downsampling	downsampling	NOUN
fcis-22002	108	30	and	and	CCONJ
fcis-22002	108	31	feature	feature	NOUN
fcis-22002	108	32	screening	screening	NOUN
fcis-22002	108	33	.	.	PUNCT
fcis-22002	109	1	the	the	DET
fcis-22002	109	2	design	design	NOUN
fcis-22002	109	3	of	of	ADP
fcis-22002	109	4	this	this	DET
fcis-22002	109	5	approach	approach	NOUN
fcis-22002	109	6	is	be	AUX
fcis-22002	109	7	inspired	inspire	VERB
fcis-22002	109	8	by	by	ADP
fcis-22002	109	9	classical	classical	ADJ
fcis-22002	109	10	convolutional	convolutional	ADJ
fcis-22002	109	11	neural	neural	ADJ
fcis-22002	109	12	network	network	NOUN
fcis-22002	109	13	architectures	architecture	NOUN
fcis-22002	109	14	,	,	PUNCT
fcis-22002	109	15	such	such	ADJ
fcis-22002	109	16	as	as	ADP
fcis-22002	109	17	vgg	vgg	NOUN
fcis-22002	109	18	and	and	CCONJ
fcis-22002	109	19	alexnet	alexnet	NOUN
fcis-22002	109	20	,	,	PUNCT
fcis-22002	109	21	which	which	PRON
fcis-22002	109	22	gradually	gradually	ADV
fcis-22002	109	23	extract	extract	VERB
fcis-22002	109	24	more	more	ADV
fcis-22002	109	25	advanced	advanced	ADJ
fcis-22002	109	26	feature	feature	NOUN
fcis-22002	109	27	representations	representation	NOUN
fcis-22002	109	28	by	by	ADP
fcis-22002	109	29	cascading	cascade	VERB
fcis-22002	109	30	convolutional	convolutional	ADJ
fcis-22002	109	31	and	and	CCONJ
fcis-22002	109	32	pooling	pool	VERB
fcis-22002	109	33	layers	layer	NOUN
fcis-22002	109	34	.	.	PUNCT
fcis-22002	110	1	in	in	ADP
fcis-22002	110	2	particular	particular	ADJ
fcis-22002	110	3	,	,	PUNCT
fcis-22002	110	4	with	with	ADP
fcis-22002	110	5	reference	reference	NOUN
fcis-22002	110	6	to	to	ADP
fcis-22002	110	7	vgg	vgg	ADJ
fcis-22002	110	8	and	and	CCONJ
fcis-22002	110	9	alexnet	alexnet	NOUN
fcis-22002	110	10	's	's	PART
fcis-22002	110	11	strategy	strategy	NOUN
fcis-22002	110	12	of	of	ADP
fcis-22002	110	13	increasing	increase	VERB
fcis-22002	110	14	the	the	DET
fcis-22002	110	15	number	number	NOUN
fcis-22002	110	16	of	of	ADP
fcis-22002	110	17	filters	filter	NOUN
fcis-22002	110	18	with	with	ADP
fcis-22002	110	19	network	network	NOUN
fcis-22002	110	20	depth	depth	NOUN
fcis-22002	110	21	,	,	PUNCT
fcis-22002	110	22	the	the	DET
fcis-22002	110	23	model	model	NOUN
fcis-22002	110	24	additionally	additionally	ADV
fcis-22002	110	25	adds	add	VERB
fcis-22002	110	26	a	a	DET
fcis-22002	110	27	fixed	fixed	ADJ
fcis-22002	110	28	number	number	NOUN
fcis-22002	110	29	of	of	ADP
fcis-22002	110	30	filters	filter	NOUN
fcis-22002	110	31	at	at	ADP
fcis-22002	110	32	each	each	DET
fcis-22002	110	33	additional	additional	ADJ
fcis-22002	110	34	layer	layer	NOUN
fcis-22002	110	35	.	.	PUNCT
fcis-22002	111	1	in	in	ADP
fcis-22002	111	2	this	this	DET
fcis-22002	111	3	way	way	NOUN
fcis-22002	111	4	,	,	PUNCT
fcis-22002	111	5	we	we	PRON
fcis-22002	111	6	retain	retain	VERB
fcis-22002	111	7	the	the	DET
fcis-22002	111	8	key	key	ADJ
fcis-22002	111	9	information	information	NOUN
fcis-22002	111	10	of	of	ADP
fcis-22002	111	11	the	the	DET
fcis-22002	111	12	acoustic	acoustic	ADJ
fcis-22002	111	13	features	feature	NOUN
fcis-22002	111	14	while	while	SCONJ
fcis-22002	111	15	reducing	reduce	VERB
fcis-22002	111	16	the	the	DET
fcis-22002	111	17	computational	computational	ADJ
fcis-22002	111	18	pressure	pressure	NOUN
fcis-22002	111	19	for	for	ADP
fcis-22002	111	20	subsequent	subsequent	ADJ
fcis-22002	111	21	tasks	task	NOUN
fcis-22002	111	22	through	through	ADP
fcis-22002	111	23	effective	effective	ADJ
fcis-22002	111	24	data	datum	NOUN
fcis-22002	111	25	dimensionality	dimensionality	NOUN
fcis-22002	111	26	reduction	reduction	NOUN
fcis-22002	111	27	.	.	PUNCT
fcis-22002	112	1	the	the	DET
fcis-22002	112	2	network	network	NOUN
fcis-22002	112	3	layer	layer	NOUN
fcis-22002	112	4	stacking	stacking	NOUN
fcis-22002	112	5	begins	begin	VERB
fcis-22002	112	6	with	with	ADP
fcis-22002	112	7	a	a	DET
fcis-22002	112	8	convolutional	convolutional	ADJ
fcis-22002	112	9	layer	layer	NOUN
fcis-22002	112	10	,	,	PUNCT
fcis-22002	112	11	which	which	PRON
fcis-22002	112	12	performs	perform	VERB
fcis-22002	112	13	feature	feature	NOUN
fcis-22002	112	14	extraction	extraction	NOUN
fcis-22002	112	15	on	on	ADP
fcis-22002	112	16	the	the	DET
fcis-22002	112	17	input	input	NOUN
fcis-22002	112	18	speech	speech	NOUN
fcis-22002	112	19	data	datum	NOUN
fcis-22002	112	20	,	,	PUNCT
fcis-22002	112	21	and	and	CCONJ
fcis-22002	112	22	these	these	DET
fcis-22002	112	23	features	feature	NOUN
fcis-22002	112	24	are	be	AUX
fcis-22002	112	25	subsequently	subsequently	ADV
fcis-22002	112	26	fed	feed	VERB
fcis-22002	112	27	into	into	ADP
fcis-22002	112	28	a	a	DET
fcis-22002	112	29	normalization	normalization	NOUN
fcis-22002	112	30	layer	layer	NOUN
fcis-22002	112	31	to	to	PART
fcis-22002	112	32	stabilize	stabilize	VERB
fcis-22002	112	33	the	the	DET
fcis-22002	112	34	training	training	NOUN
fcis-22002	112	35	process	process	NOUN
fcis-22002	112	36	and	and	CCONJ
fcis-22002	112	37	improve	improve	VERB
fcis-22002	112	38	generalization	generalization	NOUN
fcis-22002	112	39	.	.	PUNCT
fcis-22002	113	1	immediately	immediately	ADV
fcis-22002	113	2	after	after	ADV
fcis-22002	113	3	,	,	PUNCT
fcis-22002	113	4	the	the	DET
fcis-22002	113	5	activation	activation	NOUN
fcis-22002	113	6	function	function	NOUN
fcis-22002	113	7	layer	layer	NOUN
fcis-22002	113	8	introduces	introduce	NOUN
fcis-22002	113	9	nonlinearities	nonlinearitie	NOUN
fcis-22002	113	10	that	that	PRON
fcis-22002	113	11	allow	allow	VERB
fcis-22002	113	12	the	the	DET
fcis-22002	113	13	model	model	NOUN
fcis-22002	113	14	to	to	PART
fcis-22002	113	15	capture	capture	VERB
fcis-22002	113	16	more	more	ADV
fcis-22002	113	17	complex	complex	ADJ
fcis-22002	113	18	data	datum	NOUN
fcis-22002	113	19	patterns	pattern	NOUN
fcis-22002	113	20	.	.	PUNCT
fcis-22002	114	1	finally	finally	ADV
fcis-22002	114	2	,	,	PUNCT
fcis-22002	114	3	the	the	DET
fcis-22002	114	4	pooling	pool	VERB
fcis-22002	114	5	layer	layer	NOUN
fcis-22002	114	6	downsamples	downsample	VERB
fcis-22002	114	7	the	the	DET
fcis-22002	114	8	activated	activate	VERB
fcis-22002	114	9	features	feature	NOUN
fcis-22002	114	10	,	,	PUNCT
fcis-22002	114	11	effectively	effectively	ADV
fcis-22002	114	12	reducing	reduce	VERB
fcis-22002	114	13	the	the	DET
fcis-22002	114	14	data	data	NOUN
fcis-22002	114	15	dimensionality	dimensionality	NOUN
fcis-22002	114	16	while	while	SCONJ
fcis-22002	114	17	enhancing	enhance	VERB
fcis-22002	114	18	the	the	DET
fcis-22002	114	19	robustness	robustness	NOUN
fcis-22002	114	20	of	of	ADP
fcis-22002	114	21	the	the	DET
fcis-22002	114	22	model	model	NOUN
fcis-22002	114	23	to	to	ADP
fcis-22002	114	24	input	input	NOUN
fcis-22002	114	25	variations	variation	NOUN
fcis-22002	114	26	.	.	PUNCT
fcis-22002	115	1	decoder	decoder	NOUN
fcis-22002	115	2	:	:	PUNCT
fcis-22002	115	3	in	in	ADP
fcis-22002	115	4	order	order	NOUN
fcis-22002	115	5	to	to	PART
fcis-22002	115	6	retrieve	retrieve	VERB
fcis-22002	115	7	the	the	DET
fcis-22002	115	8	processed	process	VERB
fcis-22002	115	9	sequence	sequence	NOUN
fcis-22002	115	10	signals	signal	NOUN
fcis-22002	115	11	,	,	PUNCT
fcis-22002	115	12	this	this	DET
fcis-22002	115	13	paper	paper	NOUN
fcis-22002	115	14	employs	employ	VERB
fcis-22002	115	15	a	a	DET
fcis-22002	115	16	series	series	NOUN
fcis-22002	115	17	of	of	ADP
fcis-22002	115	18	transformer	transformer	ADJ
fcis-22002	115	19	blocks	block	NOUN
fcis-22002	115	20	as	as	ADP
fcis-22002	115	21	sequence	sequence	NOUN
fcis-22002	115	22	matching	matching	NOUN
fcis-22002	115	23	decoders	decoder	NOUN
fcis-22002	115	24	.	.	PUNCT
fcis-22002	116	1	transformer	transformer	NOUN
fcis-22002	116	2	blocks	block	NOUN
fcis-22002	116	3	and	and	CCONJ
fcis-22002	116	4	their	their	PRON
fcis-22002	116	5	derived	derive	VERB
fcis-22002	116	6	models	model	NOUN
fcis-22002	116	7	have	have	AUX
fcis-22002	116	8	been	be	AUX
fcis-22002	116	9	used	use	VERB
fcis-22002	116	10	in	in	ADP
fcis-22002	116	11	sequence	sequence	NOUN
fcis-22002	116	12	classification	classification	NOUN
fcis-22002	116	13	problems	problem	NOUN
fcis-22002	116	14	,	,	PUNCT
fcis-22002	116	15	and	and	CCONJ
fcis-22002	116	16	models	model	NOUN
fcis-22002	116	17	like	like	ADP
fcis-22002	116	18	bert	bert	PROPN
fcis-22002	116	19	have	have	AUX
fcis-22002	116	20	achieved	achieve	VERB
fcis-22002	116	21	commendable	commendable	ADJ
fcis-22002	116	22	results	result	NOUN
fcis-22002	116	23	in	in	ADP
fcis-22002	116	24	text	text	NOUN
fcis-22002	116	25	classification	classification	NOUN
fcis-22002	116	26	tasks	task	NOUN
fcis-22002	116	27	.	.	PUNCT
fcis-22002	117	1	when	when	SCONJ
fcis-22002	117	2	dealing	deal	VERB
fcis-22002	117	3	with	with	ADP
fcis-22002	117	4	text	text	NOUN
fcis-22002	117	5	problems	problem	NOUN
fcis-22002	117	6	,	,	PUNCT
fcis-22002	117	7	the	the	DET
fcis-22002	117	8	bert	bert	PROPN
fcis-22002	117	9	model	model	NOUN
fcis-22002	117	10	introduces	introduce	VERB
fcis-22002	117	11	a	a	DET
fcis-22002	117	12	special	special	ADJ
fcis-22002	117	13	classification	classification	NOUN
fcis-22002	117	14	token	token	VERB
fcis-22002	117	15	(	(	PUNCT
fcis-22002	117	16	[	[	X
fcis-22002	117	17	cls	cls	NOUN
fcis-22002	117	18	]	]	PUNCT
fcis-22002	117	19	)	)	PUNCT
fcis-22002	117	20	at	at	ADP
fcis-22002	117	21	the	the	DET
fcis-22002	117	22	beginning	beginning	NOUN
fcis-22002	117	23	of	of	ADP
fcis-22002	117	24	the	the	DET
fcis-22002	117	25	sequence	sequence	NOUN
fcis-22002	117	26	.	.	PUNCT
fcis-22002	118	1	borrowing	borrow	VERB
fcis-22002	118	2	from	from	ADP
fcis-22002	118	3	this	this	DET
fcis-22002	118	4	design	design	NOUN
fcis-22002	118	5	,	,	PUNCT
fcis-22002	118	6	we	we	PRON
fcis-22002	118	7	add	add	VERB
fcis-22002	118	8	a	a	DET
fcis-22002	118	9	vector	vector	NOUN
fcis-22002	118	10	with	with	ADP
fcis-22002	118	11	all	all	DET
fcis-22002	118	12	values	value	NOUN
fcis-22002	118	13	set	set	VERB
fcis-22002	118	14	to	to	ADP
fcis-22002	118	15	1	1	NUM
fcis-22002	118	16	in	in	ADP
fcis-22002	118	17	front	front	NOUN
fcis-22002	118	18	of	of	ADP
fcis-22002	118	19	the	the	DET
fcis-22002	118	20	processed	process	VERB
fcis-22002	118	21	sequence	sequence	NOUN
fcis-22002	118	22	as	as	ADP
fcis-22002	118	23	the	the	DET
fcis-22002	118	24	retrieval	retrieval	NOUN
fcis-22002	118	25	token	token	VERB
fcis-22002	118	26	for	for	ADP
fcis-22002	118	27	this	this	DET
fcis-22002	118	28	task	task	NOUN
fcis-22002	118	29	.	.	PUNCT
fcis-22002	119	1	its	its	PRON
fcis-22002	119	2	final	final	ADJ
fcis-22002	119	3	hidden	hidden	ADJ
fcis-22002	119	4	state	state	NOUN
fcis-22002	119	5	is	be	AUX
fcis-22002	119	6	considered	consider	VERB
fcis-22002	119	7	as	as	ADP
fcis-22002	119	8	the	the	DET
fcis-22002	119	9	retrieval	retrieval	NOUN
fcis-22002	119	10	feature	feature	NOUN
fcis-22002	119	11	and	and	CCONJ
fcis-22002	119	12	the	the	DET
fcis-22002	119	13	deep	deep	ADJ
fcis-22002	119	14	hash	hash	NOUN
fcis-22002	119	15	feature	feature	NOUN
fcis-22002	119	16	i.e.	i.e.	ADV
fcis-22002	119	17	,	,	PUNCT
fcis-22002	119	18	the	the	DET
fcis-22002	119	19	output	output	NOUN
fcis-22002	119	20	of	of	ADP
fcis-22002	119	21	the	the	DET
fcis-22002	119	22	model	model	NOUN
fcis-22002	119	23	.	.	PUNCT
fcis-22002	120	1	the	the	DET
fcis-22002	120	2	entire	entire	ADJ
fcis-22002	120	3	sequence	sequence	NOUN
fcis-22002	120	4	matching	matching	NOUN
fcis-22002	120	5	decoder	decoder	NOUN
fcis-22002	120	6	consists	consist	VERB
fcis-22002	120	7	of	of	ADP
fcis-22002	120	8	multiple	multiple	ADJ
fcis-22002	120	9	layers	layer	NOUN
fcis-22002	120	10	of	of	ADP
fcis-22002	120	11	transformer	transformer	NOUN
fcis-22002	120	12	blocks	block	NOUN
fcis-22002	120	13	.	.	PUNCT
fcis-22002	121	1	in	in	ADP
fcis-22002	121	2	order	order	NOUN
fcis-22002	121	3	to	to	PART
fcis-22002	121	4	enable	enable	VERB
fcis-22002	121	5	the	the	DET
fcis-22002	121	6	sequence	sequence	NOUN
fcis-22002	121	7	matching	matching	NOUN
fcis-22002	121	8	decoder	decoder	NOUN
fcis-22002	121	9	to	to	PART
fcis-22002	121	10	learn	learn	VERB
fcis-22002	121	11	the	the	DET
fcis-22002	121	12	time	time	NOUN
fcis-22002	121	13	dependent	dependent	ADJ
fcis-22002	121	14	features	feature	NOUN
fcis-22002	121	15	,	,	PUNCT
fcis-22002	121	16	we	we	PRON
fcis-22002	121	17	introduce	introduce	VERB
fcis-22002	121	18	position	position	NOUN
fcis-22002	121	19	encoding	encoding	NOUN
fcis-22002	121	20	before	before	ADP
fcis-22002	121	21	feeding	feed	VERB
fcis-22002	121	22	the	the	DET
fcis-22002	121	23	sequence	sequence	NOUN
fcis-22002	121	24	into	into	ADP
fcis-22002	121	25	the	the	DET
fcis-22002	121	26	retrieval	retrieval	NOUN
fcis-22002	121	27	module	module	NOUN
fcis-22002	121	28	.	.	PUNCT
fcis-22002	122	1	position	position	NOUN
fcis-22002	122	2	encoding	encoding	NOUN
fcis-22002	122	3	helps	help	VERB
fcis-22002	122	4	the	the	DET
fcis-22002	122	5	attention	attention	NOUN
fcis-22002	122	6	mechanism	mechanism	NOUN
fcis-22002	122	7	to	to	PART
fcis-22002	122	8	understand	understand	VERB
fcis-22002	122	9	the	the	DET
fcis-22002	122	10	positional	positional	ADJ
fcis-22002	122	11	relationships	relationship	NOUN
fcis-22002	122	12	within	within	ADP
fcis-22002	122	13	the	the	DET
fcis-22002	122	14	sequence	sequence	NOUN
fcis-22002	122	15	,	,	PUNCT
fcis-22002	122	16	which	which	PRON
fcis-22002	122	17	is	be	AUX
fcis-22002	122	18	crucial	crucial	ADJ
fcis-22002	122	19	for	for	ADP
fcis-22002	122	20	sequence	sequence	NOUN
fcis-22002	122	21	retrieval	retrieval	NOUN
fcis-22002	122	22	.	.	PUNCT
fcis-22002	123	1	the	the	DET
fcis-22002	123	2	tanh	tanh	PROPN
fcis-22002	123	3	activation	activation	NOUN
fcis-22002	123	4	function	function	NOUN
fcis-22002	123	5	is	be	AUX
fcis-22002	123	6	employed	employ	VERB
fcis-22002	123	7	to	to	PART
fcis-22002	123	8	process	process	VERB
fcis-22002	123	9	the	the	DET
fcis-22002	123	10	final	final	ADJ
fcis-22002	123	11	hidden	hidden	ADJ
fcis-22002	123	12	state	state	NOUN
fcis-22002	123	13	of	of	ADP
fcis-22002	123	14	the	the	DET
fcis-22002	123	15	retrieved	retrieve	VERB
fcis-22002	123	16	token	token	VERB
fcis-22002	123	17	.	.	PUNCT
fcis-22002	124	1	tanh	tanh	NOUN
fcis-22002	124	2	activation	activation	NOUN
fcis-22002	124	3	function	function	NOUN
fcis-22002	124	4	is	be	AUX
fcis-22002	124	5	formulated	formulate	VERB
fcis-22002	124	6	:	:	PUNCT
fcis-22002	124	7	(	(	PUNCT
fcis-22002	124	8	)	)	PUNCT
fcis-22002	124	9	x	x	PUNCT
fcis-22002	125	1	x	x	PUNCT
fcis-22002	125	2	x	x	PUNCT
fcis-22002	125	3	x	x	PUNCT
fcis-22002	125	4	e	e	X
fcis-22002	125	5	e	e	X
fcis-22002	125	6	tanh	tanh	PROPN
fcis-22002	125	7	x	x	SYM
fcis-22002	125	8	e	e	NOUN
fcis-22002	125	9	e	e	X
fcis-22002	125	10			PROPN
fcis-22002	125	11			PROPN
fcis-22002	125	12			VERB
fcis-22002	125	13			PRON
fcis-22002	125	14			PROPN
fcis-22002	125	15	,	,	PUNCT
fcis-22002	125	16	which	which	PRON
fcis-22002	125	17	is	be	AUX
fcis-22002	125	18	capable	capable	ADJ
fcis-22002	125	19	of	of	ADP
fcis-22002	125	20	restricting	restrict	VERB
fcis-22002	125	21	the	the	DET
fcis-22002	125	22	output	output	NOUN
fcis-22002	125	23	values	value	NOUN
fcis-22002	125	24	to	to	ADP
fcis-22002	125	25	the	the	DET
fcis-22002	125	26	range	range	NOUN
fcis-22002	125	27	(	(	PUNCT
fcis-22002	125	28	-1,1	-1,1	PROPN
fcis-22002	125	29	)	)	PUNCT
fcis-22002	125	30	,	,	PUNCT
fcis-22002	125	31	is	be	AUX
fcis-22002	125	32	mainly	mainly	ADV
fcis-22002	125	33	intended	intend	VERB
fcis-22002	125	34	to	to	PART
fcis-22002	125	35	facilitate	facilitate	VERB
fcis-22002	125	36	the	the	DET
fcis-22002	125	37	binarization	binarization	NOUN
fcis-22002	125	38	of	of	ADP
fcis-22002	125	39	the	the	DET
fcis-22002	125	40	model	model	NOUN
fcis-22002	125	41	's	's	PART
fcis-22002	125	42	output	output	NOUN
fcis-22002	125	43	into	into	ADP
fcis-22002	125	44	hash	hash	NOUN
fcis-22002	125	45	codes	code	NOUN
fcis-22002	125	46	.	.	PUNCT
fcis-22002	126	1	3.2	3.2	NUM
fcis-22002	126	2	.	.	PUNCT
fcis-22002	126	3	model	model	NOUN
fcis-22002	126	4	training	train	VERB
fcis-22002	126	5	a	a	DET
fcis-22002	126	6	two	two	NUM
fcis-22002	126	7	-	-	PUNCT
fcis-22002	126	8	stage	stage	NOUN
fcis-22002	126	9	training	training	NOUN
fcis-22002	126	10	strategy	strategy	NOUN
fcis-22002	126	11	was	be	AUX
fcis-22002	126	12	used	use	VERB
fcis-22002	126	13	for	for	ADP
fcis-22002	126	14	model	model	NOUN
fcis-22002	126	15	training	training	NOUN
fcis-22002	126	16	.	.	PUNCT
fcis-22002	127	1	in	in	ADP
fcis-22002	127	2	the	the	DET
fcis-22002	127	3	first	first	ADJ
fcis-22002	127	4	stage	stage	NOUN
fcis-22002	127	5	,	,	PUNCT
fcis-22002	127	6	the	the	DET
fcis-22002	127	7	wavenet	wavenet	ADJ
fcis-22002	127	8	encoder	encoder	NOUN
fcis-22002	127	9	part	part	NOUN
fcis-22002	127	10	is	be	AUX
fcis-22002	127	11	pre	pre	ADJ
fcis-22002	127	12	-	-	VERB
fcis-22002	127	13	trained	trained	ADJ
fcis-22002	127	14	.	.	PUNCT
fcis-22002	128	1	the	the	DET
fcis-22002	128	2	second	second	ADJ
fcis-22002	128	3	segment	segment	NOUN
fcis-22002	128	4	trains	train	VERB
fcis-22002	128	5	the	the	DET
fcis-22002	128	6	complete	complete	ADJ
fcis-22002	128	7	model	model	NOUN
fcis-22002	128	8	for	for	ADP
fcis-22002	128	9	classification	classification	NOUN
fcis-22002	128	10	based	base	VERB
fcis-22002	128	11	on	on	ADP
fcis-22002	128	12	speech	speech	NOUN
fcis-22002	128	13	content	content	NOUN
fcis-22002	128	14	.	.	PUNCT
fcis-22002	129	1	pre	pre	ADJ
fcis-22002	129	2	-	-	ADJ
fcis-22002	129	3	training	training	ADJ
fcis-22002	129	4	phase	phase	NOUN
fcis-22002	129	5	:	:	PUNCT
fcis-22002	129	6	first	first	ADV
fcis-22002	129	7	,	,	PUNCT
fcis-22002	129	8	at	at	ADP
fcis-22002	129	9	the	the	DET
fcis-22002	129	10	backend	backend	NOUN
fcis-22002	129	11	of	of	ADP
fcis-22002	129	12	the	the	DET
fcis-22002	129	13	encoder	encoder	NOUN
fcis-22002	129	14	,	,	PUNCT
fcis-22002	129	15	we	we	PRON
fcis-22002	129	16	added	add	VERB
fcis-22002	129	17	a	a	DET
fcis-22002	129	18	one	one	NUM
fcis-22002	129	19	-	-	PUNCT
fcis-22002	129	20	dimensional	dimensional	ADJ
fcis-22002	129	21	convolutional	convolutional	ADJ
fcis-22002	129	22	layer	layer	NOUN
fcis-22002	129	23	aimed	aim	VERB
fcis-22002	129	24	at	at	ADP
fcis-22002	129	25	obtaining	obtain	VERB
fcis-22002	129	26	the	the	DET
fcis-22002	129	27	word	word	NOUN
fcis-22002	129	28	probability	probability	NOUN
fcis-22002	129	29	distribution	distribution	NOUN
fcis-22002	129	30	for	for	ADP
fcis-22002	129	31	each	each	DET
fcis-22002	129	32	frame	frame	NOUN
fcis-22002	129	33	.	.	PUNCT
fcis-22002	130	1	subsequently	subsequently	ADV
fcis-22002	130	2	,	,	PUNCT
fcis-22002	130	3	training	training	NOUN
fcis-22002	130	4	is	be	AUX
fcis-22002	130	5	performed	perform	VERB
fcis-22002	130	6	using	use	VERB
fcis-22002	130	7	this	this	DET
fcis-22002	130	8	newly	newly	ADV
fcis-22002	130	9	added	add	VERB
fcis-22002	130	10	model	model	NOUN
fcis-22002	130	11	structure	structure	NOUN
fcis-22002	130	12	with	with	ADP
fcis-22002	130	13	a	a	DET
fcis-22002	130	14	connected	connected	ADJ
fcis-22002	130	15	timing	timing	NOUN
fcis-22002	130	16	classification	classification	NOUN
fcis-22002	130	17	(	(	PUNCT
fcis-22002	130	18	ctc	ctc	NOUN
fcis-22002	130	19	)	)	PUNCT
fcis-22002	130	20	loss	loss	NOUN
fcis-22002	130	21	function	function	NOUN
fcis-22002	130	22	.	.	PUNCT
fcis-22002	131	1	pre	pre	ADJ
fcis-22002	131	2	-	-	ADJ
fcis-22002	131	3	training	training	NOUN
fcis-22002	131	4	was	be	AUX
fcis-22002	131	5	performed	perform	VERB
fcis-22002	131	6	using	use	VERB
fcis-22002	131	7	the	the	DET
fcis-22002	131	8	same	same	ADJ
fcis-22002	131	9	dataset	dataset	NOUN
fcis-22002	131	10	as	as	ADP
fcis-22002	131	11	the	the	DET
fcis-22002	131	12	classification	classification	NOUN
fcis-22002	131	13	training	training	NOUN
fcis-22002	131	14	performed	perform	VERB
fcis-22002	131	15	later	later	ADV
fcis-22002	131	16	.	.	PUNCT
fcis-22002	132	1	complete	complete	ADJ
fcis-22002	132	2	model	model	NOUN
fcis-22002	132	3	training	training	NOUN
fcis-22002	132	4	:	:	PUNCT
fcis-22002	132	5	our	our	PRON
fcis-22002	132	6	training	training	NOUN
fcis-22002	132	7	goal	goal	NOUN
fcis-22002	132	8	is	be	AUX
fcis-22002	132	9	to	to	PART
fcis-22002	132	10	ensure	ensure	VERB
fcis-22002	132	11	that	that	SCONJ
fcis-22002	132	12	the	the	DET
fcis-22002	132	13	model	model	NOUN
fcis-22002	132	14	is	be	AUX
fcis-22002	132	15	able	able	ADJ
fcis-22002	132	16	to	to	PART
fcis-22002	132	17	generate	generate	VERB
fcis-22002	132	18	accurate	accurate	ADJ
fcis-22002	132	19	hash	hash	NOUN
fcis-22002	132	20	codes	code	NOUN
fcis-22002	132	21	for	for	ADP
fcis-22002	132	22	specific	specific	ADJ
fcis-22002	132	23	speech	speech	NOUN
fcis-22002	132	24	segments	segment	NOUN
fcis-22002	132	25	.	.	PUNCT
fcis-22002	133	1	to	to	PART
fcis-22002	133	2	achieve	achieve	VERB
fcis-22002	133	3	this	this	DET
fcis-22002	133	4	goal	goal	NOUN
fcis-22002	133	5	,	,	PUNCT
fcis-22002	133	6	a	a	DET
fcis-22002	133	7	classification	classification	NOUN
fcis-22002	133	8	task	task	NOUN
fcis-22002	133	9	is	be	AUX
fcis-22002	133	10	used	use	VERB
fcis-22002	133	11	as	as	ADP
fcis-22002	133	12	the	the	DET
fcis-22002	133	13	main	main	ADJ
fcis-22002	133	14	training	training	NOUN
fcis-22002	133	15	strategy	strategy	NOUN
fcis-22002	133	16	.	.	PUNCT
fcis-22002	134	1	specifically	specifically	ADV
fcis-22002	134	2	,	,	PUNCT
fcis-22002	134	3	speech	speech	NOUN
fcis-22002	134	4	is	be	AUX
fcis-22002	134	5	categorized	categorize	VERB
fcis-22002	134	6	based	base	VERB
fcis-22002	134	7	on	on	ADP
fcis-22002	134	8	the	the	DET
fcis-22002	134	9	textual	textual	ADJ
fcis-22002	134	10	information	information	NOUN
fcis-22002	134	11	of	of	ADP
fcis-22002	134	12	the	the	DET
fcis-22002	134	13	speech	speech	NOUN
fcis-22002	134	14	content	content	NOUN
fcis-22002	134	15	,	,	PUNCT
fcis-22002	134	16	and	and	CCONJ
fcis-22002	134	17	the	the	DET
fcis-22002	134	18	same	same	ADJ
fcis-22002	134	19	textual	textual	ADJ
fcis-22002	134	20	information	information	NOUN
fcis-22002	134	21	uttered	utter	VERB
fcis-22002	134	22	by	by	ADP
fcis-22002	134	23	different	different	ADJ
fcis-22002	134	24	25	25	NUM
fcis-22002	134	25	individuals	individual	NOUN
fcis-22002	134	26	is	be	AUX
fcis-22002	134	27	considered	consider	VERB
fcis-22002	134	28	as	as	ADP
fcis-22002	134	29	the	the	DET
fcis-22002	134	30	same	same	ADJ
fcis-22002	134	31	category	category	NOUN
fcis-22002	134	32	.	.	PUNCT
fcis-22002	135	1	in	in	ADP
fcis-22002	135	2	the	the	DET
fcis-22002	135	3	model	model	NOUN
fcis-22002	135	4	architecture	architecture	NOUN
fcis-22002	135	5	,	,	PUNCT
fcis-22002	135	6	a	a	DET
fcis-22002	135	7	fully	fully	ADV
fcis-22002	135	8	-	-	PUNCT
fcis-22002	135	9	connected	connect	VERB
fcis-22002	135	10	layer	layer	NOUN
fcis-22002	135	11	is	be	AUX
fcis-22002	135	12	added	add	VERB
fcis-22002	135	13	to	to	ADP
fcis-22002	135	14	the	the	DET
fcis-22002	135	15	back	back	NOUN
fcis-22002	135	16	-	-	PUNCT
fcis-22002	135	17	end	end	NOUN
fcis-22002	135	18	of	of	ADP
fcis-22002	135	19	the	the	DET
fcis-22002	135	20	model	model	NOUN
fcis-22002	135	21	to	to	PART
fcis-22002	135	22	accommodate	accommodate	VERB
fcis-22002	135	23	the	the	DET
fcis-22002	135	24	classification	classification	NOUN
fcis-22002	135	25	task	task	NOUN
fcis-22002	135	26	.	.	PUNCT
fcis-22002	136	1	this	this	DET
fcis-22002	136	2	layer	layer	NOUN
fcis-22002	136	3	is	be	AUX
fcis-22002	136	4	designed	design	VERB
fcis-22002	136	5	so	so	SCONJ
fcis-22002	136	6	that	that	SCONJ
fcis-22002	136	7	the	the	DET
fcis-22002	136	8	model	model	NOUN
fcis-22002	136	9	can	can	AUX
fcis-22002	136	10	output	output	VERB
fcis-22002	136	11	a	a	DET
fcis-22002	136	12	vector	vector	NOUN
fcis-22002	136	13	containing	contain	VERB
fcis-22002	136	14	the	the	DET
fcis-22002	136	15	probability	probability	NOUN
fcis-22002	136	16	of	of	ADP
fcis-22002	136	17	each	each	DET
fcis-22002	136	18	category	category	NOUN
fcis-22002	136	19	.	.	PUNCT
fcis-22002	137	1	through	through	ADP
fcis-22002	137	2	the	the	DET
fcis-22002	137	3	application	application	NOUN
fcis-22002	137	4	of	of	ADP
fcis-22002	137	5	a	a	DET
fcis-22002	137	6	softmax	softmax	NOUN
fcis-22002	137	7	activation	activation	NOUN
fcis-22002	137	8	function	function	NOUN
fcis-22002	137	9	,	,	PUNCT
fcis-22002	137	10	these	these	DET
fcis-22002	137	11	outputs	output	NOUN
fcis-22002	137	12	are	be	AUX
fcis-22002	137	13	converted	convert	VERB
fcis-22002	137	14	into	into	ADP
fcis-22002	137	15	probability	probability	NOUN
fcis-22002	137	16	distributions	distribution	NOUN
fcis-22002	137	17	where	where	SCONJ
fcis-22002	137	18	each	each	DET
fcis-22002	137	19	element	element	NOUN
fcis-22002	137	20	represents	represent	VERB
fcis-22002	137	21	the	the	DET
fcis-22002	137	22	predicted	predict	VERB
fcis-22002	137	23	probability	probability	NOUN
fcis-22002	137	24	of	of	ADP
fcis-22002	137	25	the	the	DET
fcis-22002	137	26	corresponding	correspond	VERB
fcis-22002	137	27	category	category	NOUN
fcis-22002	137	28	.	.	PUNCT
fcis-22002	138	1	immediately	immediately	ADV
fcis-22002	138	2	thereafter	thereafter	ADV
fcis-22002	138	3	,	,	PUNCT
fcis-22002	138	4	the	the	DET
fcis-22002	138	5	cross	cross	ADJ
fcis-22002	138	6	-	-	ADJ
fcis-22002	138	7	entropy	entropy	ADJ
fcis-22002	138	8	loss	loss	NOUN
fcis-22002	138	9	function	function	NOUN
fcis-22002	138	10	is	be	AUX
fcis-22002	138	11	utilized	utilize	VERB
fcis-22002	138	12	to	to	PART
fcis-22002	138	13	evaluate	evaluate	VERB
fcis-22002	138	14	the	the	DET
fcis-22002	138	15	deviation	deviation	NOUN
fcis-22002	138	16	between	between	ADP
fcis-22002	138	17	the	the	DET
fcis-22002	138	18	probability	probability	NOUN
fcis-22002	138	19	distribution	distribution	NOUN
fcis-22002	138	20	predicted	predict	VERB
fcis-22002	138	21	by	by	ADP
fcis-22002	138	22	the	the	DET
fcis-22002	138	23	model	model	NOUN
fcis-22002	138	24	and	and	CCONJ
fcis-22002	138	25	the	the	DET
fcis-22002	138	26	true	true	ADJ
fcis-22002	138	27	labels	label	NOUN
fcis-22002	138	28	,	,	PUNCT
fcis-22002	138	29	an	an	DET
fcis-22002	138	30	approach	approach	NOUN
fcis-22002	138	31	that	that	PRON
fcis-22002	138	32	accurately	accurately	ADV
fcis-22002	138	33	quantifies	quantify	VERB
fcis-22002	138	34	the	the	DET
fcis-22002	138	35	performance	performance	NOUN
fcis-22002	138	36	of	of	ADP
fcis-22002	138	37	the	the	DET
fcis-22002	138	38	model	model	NOUN
fcis-22002	138	39	on	on	ADP
fcis-22002	138	40	the	the	DET
fcis-22002	138	41	classification	classification	NOUN
fcis-22002	138	42	task	task	NOUN
fcis-22002	138	43	.	.	PUNCT
fcis-22002	139	1	notably	notably	ADV
fcis-22002	139	2	,	,	PUNCT
fcis-22002	139	3	despite	despite	SCONJ
fcis-22002	139	4	starting	start	VERB
fcis-22002	139	5	with	with	ADP
fcis-22002	139	6	a	a	DET
fcis-22002	139	7	pre	pre	ADJ
fcis-22002	139	8	-	-	ADJ
fcis-22002	139	9	trained	train	VERB
fcis-22002	139	10	wavenet	wavenet	NOUN
fcis-22002	139	11	model	model	NOUN
fcis-22002	139	12	,	,	PUNCT
fcis-22002	139	13	the	the	DET
fcis-22002	139	14	weights	weight	NOUN
fcis-22002	139	15	of	of	ADP
fcis-22002	139	16	the	the	DET
fcis-22002	139	17	pre	pre	ADJ
fcis-22002	139	18	-	-	ADJ
fcis-22002	139	19	trained	train	VERB
fcis-22002	139	20	portion	portion	NOUN
fcis-22002	139	21	are	be	AUX
fcis-22002	139	22	not	not	PART
fcis-22002	139	23	frozen	freeze	VERB
fcis-22002	139	24	during	during	ADP
fcis-22002	139	25	subsequent	subsequent	ADJ
fcis-22002	139	26	training	training	NOUN
fcis-22002	139	27	.	.	PUNCT
fcis-22002	140	1	instead	instead	ADV
fcis-22002	140	2	,	,	PUNCT
fcis-22002	140	3	the	the	DET
fcis-22002	140	4	entire	entire	ADJ
fcis-22002	140	5	model	model	NOUN
fcis-22002	140	6	is	be	AUX
fcis-22002	140	7	fine	fine	ADV
fcis-22002	140	8	-	-	PUNCT
fcis-22002	140	9	tuned	tune	VERB
fcis-22002	140	10	to	to	PART
fcis-22002	140	11	ensure	ensure	VERB
fcis-22002	140	12	optimal	optimal	ADJ
fcis-22002	140	13	performance	performance	NOUN
fcis-22002	140	14	on	on	ADP
fcis-22002	140	15	specific	specific	ADJ
fcis-22002	140	16	speech	speech	NOUN
fcis-22002	140	17	retrieval	retrieval	NOUN
fcis-22002	140	18	tasks	task	NOUN
fcis-22002	140	19	.	.	PUNCT
fcis-22002	141	1	this	this	DET
fcis-22002	141	2	finetuning	finetune	VERB
fcis-22002	141	3	strategy	strategy	NOUN
fcis-22002	141	4	allows	allow	VERB
fcis-22002	141	5	the	the	DET
fcis-22002	141	6	model	model	NOUN
fcis-22002	141	7	to	to	PART
fcis-22002	141	8	combine	combine	VERB
fcis-22002	141	9	the	the	DET
fcis-22002	141	10	knowledge	knowledge	NOUN
fcis-22002	141	11	learned	learn	VERB
fcis-22002	141	12	from	from	ADP
fcis-22002	141	13	the	the	DET
fcis-22002	141	14	original	original	ADJ
fcis-22002	141	15	pre	pre	ADJ
fcis-22002	141	16	-	-	ADJ
fcis-22002	141	17	training	training	ADJ
fcis-22002	141	18	task	task	NOUN
fcis-22002	141	19	with	with	ADP
fcis-22002	141	20	the	the	DET
fcis-22002	141	21	data	datum	NOUN
fcis-22002	141	22	from	from	ADP
fcis-22002	141	23	the	the	DET
fcis-22002	141	24	new	new	ADJ
fcis-22002	141	25	task	task	NOUN
fcis-22002	141	26	to	to	PART
fcis-22002	141	27	achieve	achieve	VERB
fcis-22002	141	28	better	well	ADJ
fcis-22002	141	29	generalization	generalization	NOUN
fcis-22002	141	30	performance	performance	NOUN
fcis-22002	141	31	.	.	PUNCT
fcis-22002	142	1	the	the	DET
fcis-22002	142	2	details	detail	NOUN
fcis-22002	142	3	of	of	ADP
fcis-22002	142	4	the	the	DET
fcis-22002	142	5	model	model	NOUN
fcis-22002	142	6	are	be	AUX
fcis-22002	142	7	shown	show	VERB
fcis-22002	142	8	in	in	ADP
fcis-22002	142	9	figure	figure	NOUN
fcis-22002	142	10	1	1	NUM
fcis-22002	142	11	.	.	PUNCT
fcis-22002	143	1	figure	figure	NOUN
fcis-22002	143	2	1	1	NUM
fcis-22002	143	3	.	.	PUNCT
fcis-22002	144	1	the	the	DET
fcis-22002	144	2	model	model	NOUN
fcis-22002	144	3	3.3	3.3	NUM
fcis-22002	144	4	.	.	PUNCT
fcis-22002	145	1	hash	hash	NOUN
fcis-22002	145	2	construction	construction	NOUN
fcis-22002	145	3	to	to	PART
fcis-22002	145	4	generate	generate	VERB
fcis-22002	145	5	the	the	DET
fcis-22002	145	6	hash	hash	NOUN
fcis-22002	145	7	code	code	NOUN
fcis-22002	145	8	,	,	PUNCT
fcis-22002	145	9	the	the	DET
fcis-22002	145	10	model	model	NOUN
fcis-22002	145	11	passes	pass	VERB
fcis-22002	145	12	the	the	DET
fcis-22002	145	13	speech	speech	NOUN
fcis-22002	145	14	fragment	fragment	NOUN
fcis-22002	145	15	through	through	ADP
fcis-22002	145	16	the	the	DET
fcis-22002	145	17	model	model	NOUN
fcis-22002	145	18	through	through	ADP
fcis-22002	145	19	forward	forward	ADJ
fcis-22002	145	20	propagation	propagation	NOUN
fcis-22002	145	21	to	to	ADP
fcis-22002	145	22	the	the	DET
fcis-22002	145	23	output	output	NOUN
fcis-22002	145	24	.	.	PUNCT
fcis-22002	146	1	this	this	DET
fcis-22002	146	2	output	output	NOUN
fcis-22002	146	3	is	be	AUX
fcis-22002	146	4	a	a	DET
fcis-22002	146	5	continuous	continuous	ADJ
fcis-22002	146	6	value	value	NOUN
fcis-22002	146	7	whose	whose	DET
fcis-22002	146	8	range	range	NOUN
fcis-22002	146	9	lies	lie	VERB
fcis-22002	146	10	between	between	ADP
fcis-22002	146	11	[	[	X
fcis-22002	146	12	-1,1	-1,1	NOUN
fcis-22002	146	13	]	]	X
fcis-22002	146	14	.	.	PUNCT
fcis-22002	147	1	to	to	PART
fcis-22002	147	2	convert	convert	VERB
fcis-22002	147	3	these	these	DET
fcis-22002	147	4	continuous	continuous	ADJ
fcis-22002	147	5	values	value	NOUN
fcis-22002	147	6	into	into	ADP
fcis-22002	147	7	binary	binary	ADJ
fcis-22002	147	8	hash	hash	NOUN
fcis-22002	147	9	codes	code	NOUN
fcis-22002	147	10	,	,	PUNCT
fcis-22002	147	11	we	we	PRON
fcis-22002	147	12	employ	employ	VERB
fcis-22002	147	13	a	a	DET
fcis-22002	147	14	simple	simple	ADJ
fcis-22002	147	15	thresholding	thresholding	NOUN
fcis-22002	147	16	strategy	strategy	NOUN
fcis-22002	147	17	:	:	PUNCT
fcis-22002	147	18	for	for	ADP
fcis-22002	147	19	values	value	NOUN
fcis-22002	147	20	greater	great	ADJ
fcis-22002	147	21	than	than	ADP
fcis-22002	147	22	0	0	NUM
fcis-22002	147	23	,	,	PUNCT
fcis-22002	147	24	we	we	PRON
fcis-22002	147	25	convert	convert	VERB
fcis-22002	147	26	them	they	PRON
fcis-22002	147	27	to	to	ADP
fcis-22002	147	28	1	1	NUM
fcis-22002	147	29	;	;	PUNCT
fcis-22002	147	30	for	for	ADP
fcis-22002	147	31	values	value	NOUN
fcis-22002	147	32	less	less	ADJ
fcis-22002	147	33	than	than	ADP
fcis-22002	147	34	or	or	CCONJ
fcis-22002	147	35	equal	equal	ADJ
fcis-22002	147	36	to	to	ADP
fcis-22002	147	37	0	0	NUM
fcis-22002	147	38	,	,	PUNCT
fcis-22002	147	39	we	we	PRON
fcis-22002	147	40	convert	convert	VERB
fcis-22002	147	41	them	they	PRON
fcis-22002	147	42	to	to	ADP
fcis-22002	147	43	0	0	NUM
fcis-22002	147	44	.	.	PUNCT
fcis-22002	148	1	specifically	specifically	ADV
fcis-22002	148	2	,	,	PUNCT
fcis-22002	148	3	given	give	VERB
fcis-22002	148	4	the	the	DET
fcis-22002	148	5	model	model	NOUN
fcis-22002	148	6	's	's	PART
fcis-22002	148	7	modeled	model	VERB
fcis-22002	148	8	output	output	NOUN
fcis-22002	148	9	y	y	PROPN
fcis-22002	148	10	for	for	ADP
fcis-22002	148	11	a	a	DET
fcis-22002	148	12	given	give	VERB
fcis-22002	148	13	speech	speech	NOUN
fcis-22002	148	14	fragment	fragment	NOUN
fcis-22002	148	15	,	,	PUNCT
fcis-22002	148	16	its	its	PRON
fcis-22002	148	17	corresponding	corresponding	ADJ
fcis-22002	148	18	binary	binary	ADJ
fcis-22002	148	19	hash	hash	PROPN
fcis-22002	148	20	code	code	PROPN
fcis-22002	148	21	b	b	NOUN
fcis-22002	148	22	can	can	AUX
fcis-22002	148	23	be	be	AUX
fcis-22002	148	24	obtained	obtain	VERB
fcis-22002	148	25	as	as	ADP
fcis-22002	148	26	follows	follow	VERB
fcis-22002	148	27	:	:	PUNCT
fcis-22002	148	28	figure	figure	NOUN
fcis-22002	148	29	2	2	NUM
fcis-22002	148	30	.	.	PUNCT
fcis-22002	148	31	retrieval	retrieval	NOUN
fcis-22002	148	32	strategy	strategy	NOUN
fcis-22002	149	1	i	i	PRON
fcis-22002	149	2	1	1	NUM
fcis-22002	149	3	if	if	SCONJ
fcis-22002	149	4	y	y	PROPN
fcis-22002	149	5	>	>	X
fcis-22002	149	6	0	0	PUNCT
fcis-22002	149	7	ib	ib	NOUN
fcis-22002	149	8	otherwise	otherwise	ADV
fcis-22002	149	9			ADV
fcis-22002	149	10			NOUN
fcis-22002	150	1			NOUN
fcis-22002	150	2			NOUN
fcis-22002	150	3	0	0	NUM
fcis-22002	150	4	3.4	3.4	NUM
fcis-22002	150	5	.	.	PUNCT
fcis-22002	150	6	retrieval	retrieval	NOUN
fcis-22002	150	7	strategy	strategy	NOUN
fcis-22002	150	8	this	this	DET
fcis-22002	150	9	study	study	NOUN
fcis-22002	150	10	proposes	propose	VERB
fcis-22002	150	11	an	an	DET
fcis-22002	150	12	end	end	NOUN
fcis-22002	150	13	-	-	PUNCT
fcis-22002	150	14	to	to	ADP
fcis-22002	150	15	-	-	PUNCT
fcis-22002	150	16	end	end	NOUN
fcis-22002	150	17	framework	framework	NOUN
fcis-22002	150	18	that	that	PRON
fcis-22002	150	19	accurately	accurately	ADV
fcis-22002	150	20	maps	map	VERB
fcis-22002	150	21	text	text	NOUN
fcis-22002	150	22	-	-	PUNCT
fcis-22002	150	23	related	relate	VERB
fcis-22002	150	24	content	content	NOUN
fcis-22002	150	25	in	in	ADP
fcis-22002	150	26	speech	speech	NOUN
fcis-22002	150	27	to	to	ADP
fcis-22002	150	28	binary	binary	ADJ
fcis-22002	150	29	hash	hash	NOUN
fcis-22002	150	30	codes	code	NOUN
fcis-22002	150	31	through	through	ADP
fcis-22002	150	32	deep	deep	ADJ
fcis-22002	150	33	learning	learning	NOUN
fcis-22002	150	34	techniques	technique	NOUN
fcis-22002	150	35	.	.	PUNCT
fcis-22002	151	1	the	the	DET
fcis-22002	151	2	core	core	NOUN
fcis-22002	151	3	of	of	ADP
fcis-22002	151	4	the	the	DET
fcis-22002	151	5	approach	approach	NOUN
fcis-22002	151	6	is	be	AUX
fcis-22002	151	7	that	that	SCONJ
fcis-22002	151	8	it	it	PRON
fcis-22002	151	9	not	not	PART
fcis-22002	151	10	only	only	ADV
fcis-22002	151	11	learns	learn	VERB
fcis-22002	151	12	the	the	DET
fcis-22002	151	13	mapping	mapping	NOUN
fcis-22002	151	14	relationship	relationship	NOUN
fcis-22002	151	15	from	from	ADP
fcis-22002	151	16	speech	speech	NOUN
fcis-22002	151	17	signals	signal	NOUN
fcis-22002	151	18	to	to	ADP
fcis-22002	151	19	textual	textual	ADJ
fcis-22002	151	20	content	content	NOUN
fcis-22002	151	21	,	,	PUNCT
fcis-22002	151	22	but	but	CCONJ
fcis-22002	151	23	is	be	AUX
fcis-22002	151	24	also	also	ADV
fcis-22002	151	25	able	able	ADJ
fcis-22002	151	26	to	to	PART
fcis-22002	151	27	convert	convert	VERB
fcis-22002	151	28	this	this	DET
fcis-22002	151	29	mapping	mapping	NOUN
fcis-22002	151	30	relationship	relationship	NOUN
fcis-22002	151	31	directly	directly	ADV
fcis-22002	151	32	into	into	ADP
fcis-22002	151	33	binary	binary	ADJ
fcis-22002	151	34	hash	hash	NOUN
fcis-22002	151	35	codes	code	NOUN
fcis-22002	151	36	.	.	PUNCT
fcis-22002	152	1	before	before	ADP
fcis-22002	152	2	performing	perform	VERB
fcis-22002	152	3	the	the	DET
fcis-22002	152	4	retrieval	retrieval	NOUN
fcis-22002	152	5	task	task	NOUN
fcis-22002	152	6	,	,	PUNCT
fcis-22002	152	7	the	the	DET
fcis-22002	152	8	speech	speech	NOUN
fcis-22002	152	9	data	datum	NOUN
fcis-22002	152	10	in	in	ADP
fcis-22002	152	11	the	the	DET
fcis-22002	152	12	database	database	NOUN
fcis-22002	152	13	is	be	AUX
fcis-22002	152	14	preprocessed	preprocesse	VERB
fcis-22002	152	15	to	to	PART
fcis-22002	152	16	generate	generate	VERB
fcis-22002	152	17	a	a	DET
fcis-22002	152	18	hash	hash	NOUN
fcis-22002	152	19	index	index	NOUN
fcis-22002	152	20	table	table	NOUN
fcis-22002	152	21	.	.	PUNCT
fcis-22002	153	1	in	in	ADP
fcis-22002	153	2	the	the	DET
fcis-22002	153	3	retrieval	retrieval	NOUN
fcis-22002	153	4	phase	phase	NOUN
fcis-22002	153	5	,	,	PUNCT
fcis-22002	153	6	for	for	ADP
fcis-22002	153	7	a	a	DET
fcis-22002	153	8	target	target	NOUN
fcis-22002	153	9	speech	speech	NOUN
fcis-22002	153	10	,	,	PUNCT
fcis-22002	153	11	its	its	PRON
fcis-22002	153	12	binary	binary	ADJ
fcis-22002	153	13	hash	hash	NOUN
fcis-22002	153	14	code	code	PROPN
fcis-22002	153	15	is	be	AUX
fcis-22002	153	16	first	first	ADV
fcis-22002	153	17	generated	generate	VERB
fcis-22002	153	18	by	by	ADP
fcis-22002	153	19	the	the	DET
fcis-22002	153	20	above	above	ADJ
fcis-22002	153	21	model	model	NOUN
fcis-22002	153	22	,	,	PUNCT
fcis-22002	153	23	and	and	CCONJ
fcis-22002	153	24	then	then	ADV
fcis-22002	153	25	this	this	DET
fcis-22002	153	26	hash	hash	NOUN
fcis-22002	153	27	code	code	NOUN
fcis-22002	153	28	is	be	AUX
fcis-22002	153	29	compared	compare	VERB
fcis-22002	153	30	with	with	ADP
fcis-22002	153	31	the	the	DET
fcis-22002	153	32	pre	pre	ADJ
fcis-22002	153	33	-	-	ADJ
fcis-22002	153	34	stored	store	VERB
fcis-22002	153	35	hash	hash	NOUN
fcis-22002	153	36	code	code	NOUN
fcis-22002	153	37	index	index	NOUN
fcis-22002	153	38	table	table	NOUN
fcis-22002	153	39	in	in	ADP
fcis-22002	153	40	the	the	DET
fcis-22002	153	41	database	database	NOUN
fcis-22002	153	42	.	.	PUNCT
fcis-22002	154	1	by	by	ADP
fcis-22002	154	2	calculating	calculate	VERB
fcis-22002	154	3	the	the	DET
fcis-22002	154	4	bit	bit	NOUN
fcis-22002	154	5	error	error	NOUN
fcis-22002	154	6	rate	rate	NOUN
fcis-22002	154	7	(	(	PUNCT
fcis-22002	154	8	ber	ber	PROPN
fcis-22002	154	9	)	)	PUNCT
fcis-22002	154	10	,	,	PUNCT
fcis-22002	154	11	we	we	PRON
fcis-22002	154	12	can	can	AUX
fcis-22002	154	13	quickly	quickly	ADV
fcis-22002	154	14	identify	identify	VERB
fcis-22002	154	15	the	the	DET
fcis-22002	154	16	speech	speech	NOUN
fcis-22002	154	17	segments	segment	NOUN
fcis-22002	154	18	that	that	PRON
fcis-22002	154	19	are	be	AUX
fcis-22002	154	20	most	most	ADV
fcis-22002	154	21	similar	similar	ADJ
fcis-22002	154	22	in	in	ADP
fcis-22002	154	23	content	content	NOUN
fcis-22002	154	24	to	to	ADP
fcis-22002	154	25	the	the	DET
fcis-22002	154	26	target	target	NOUN
fcis-22002	154	27	speech	speech	NOUN
fcis-22002	154	28	.	.	PUNCT
fcis-22002	155	1	this	this	DET
fcis-22002	155	2	approach	approach	NOUN
fcis-22002	155	3	makes	make	VERB
fcis-22002	155	4	it	it	PRON
fcis-22002	155	5	possible	possible	ADJ
fcis-22002	155	6	to	to	PART
fcis-22002	155	7	perform	perform	VERB
fcis-22002	155	8	content	content	ADJ
fcis-22002	155	9	-	-	PUNCT
fcis-22002	155	10	similar	similar	ADJ
fcis-22002	155	11	speech	speech	NOUN
fcis-22002	155	12	retrieval	retrieval	NOUN
fcis-22002	155	13	with	with	ADP
fcis-22002	155	14	great	great	ADJ
fcis-22002	155	15	efficiency	efficiency	NOUN
fcis-22002	155	16	and	and	CCONJ
fcis-22002	155	17	accuracy	accuracy	NOUN
fcis-22002	155	18	even	even	ADV
fcis-22002	155	19	in	in	ADP
fcis-22002	155	20	huge	huge	ADJ
fcis-22002	155	21	speech	speech	NOUN
fcis-22002	155	22	databases	database	NOUN
fcis-22002	155	23	,	,	PUNCT
fcis-22002	155	24	substantially	substantially	ADV
fcis-22002	155	25	improving	improve	VERB
fcis-22002	155	26	the	the	DET
fcis-22002	155	27	performance	performance	NOUN
fcis-22002	155	28	of	of	ADP
fcis-22002	155	29	speech	speech	NOUN
fcis-22002	155	30	retrieval	retrieval	NOUN
fcis-22002	155	31	systems	system	NOUN
fcis-22002	155	32	.	.	PUNCT
fcis-22002	156	1	the	the	DET
fcis-22002	156	2	retrieval	retrieval	NOUN
fcis-22002	156	3	framework	framework	NOUN
fcis-22002	156	4	is	be	AUX
fcis-22002	156	5	shown	show	VERB
fcis-22002	156	6	in	in	ADP
fcis-22002	156	7	figure	figure	NOUN
fcis-22002	156	8	2	2	NUM
fcis-22002	156	9	4	4	NUM
fcis-22002	156	10	.	.	PUNCT
fcis-22002	156	11	experiment	experiment	NOUN
fcis-22002	156	12	4.1	4.1	NUM
fcis-22002	156	13	.	.	PUNCT
fcis-22002	157	1	experimental	experimental	ADJ
fcis-22002	157	2	setup	setup	NOUN
fcis-22002	157	3	4.1.1	4.1.1	X
fcis-22002	157	4	.	.	PUNCT
fcis-22002	158	1	database	database	VERB
fcis-22002	158	2	the	the	DET
fcis-22002	158	3	experiments	experiment	NOUN
fcis-22002	158	4	in	in	ADP
fcis-22002	158	5	this	this	DET
fcis-22002	158	6	thesis	thesis	NOUN
fcis-22002	158	7	use	use	VERB
fcis-22002	158	8	the	the	DET
fcis-22002	158	9	thchs30	thchs30	ADJ
fcis-22002	158	10	chinese	chinese	ADJ
fcis-22002	158	11	26	26	NUM
fcis-22002	158	12	speech	speech	NOUN
fcis-22002	158	13	database	database	NOUN
fcis-22002	159	1	[	[	X
fcis-22002	159	2	24	24	NUM
fcis-22002	159	3	]	]	PUNCT
fcis-22002	159	4	,	,	PUNCT
fcis-22002	159	5	whose	whose	DET
fcis-22002	159	6	selected	select	VERB
fcis-22002	159	7	speech	speech	NOUN
fcis-22002	159	8	content	content	NOUN
fcis-22002	159	9	is	be	AUX
fcis-22002	159	10	derived	derive	VERB
fcis-22002	159	11	from	from	ADP
fcis-22002	159	12	a	a	DET
fcis-22002	159	13	large	large	ADJ
fcis-22002	159	14	number	number	NOUN
fcis-22002	159	15	of	of	ADP
fcis-22002	159	16	news	news	NOUN
fcis-22002	159	17	sources	source	NOUN
fcis-22002	159	18	.	.	PUNCT
fcis-22002	160	1	based	base	VERB
fcis-22002	160	2	on	on	ADP
fcis-22002	160	3	the	the	DET
fcis-22002	160	4	speech	speech	NOUN
fcis-22002	160	5	content	content	NOUN
fcis-22002	160	6	,	,	PUNCT
fcis-22002	160	7	the	the	DET
fcis-22002	160	8	dataset	dataset	NOUN
fcis-22002	160	9	was	be	AUX
fcis-22002	160	10	divided	divide	VERB
fcis-22002	160	11	into	into	ADP
fcis-22002	160	12	four	four	NUM
fcis-22002	160	13	groups	group	NOUN
fcis-22002	160	14	,	,	PUNCT
fcis-22002	160	15	a	a	DET
fcis-22002	160	16	,	,	PUNCT
fcis-22002	160	17	b	b	NOUN
fcis-22002	160	18	,	,	PUNCT
fcis-22002	160	19	c	c	NOUN
fcis-22002	160	20	,	,	PUNCT
fcis-22002	160	21	and	and	CCONJ
fcis-22002	160	22	d.	d.	NOUN
fcis-22002	160	23	the	the	DET
fcis-22002	160	24	combination	combination	NOUN
fcis-22002	160	25	of	of	ADP
fcis-22002	160	26	words	word	NOUN
fcis-22002	160	27	in	in	ADP
fcis-22002	160	28	groups	group	NOUN
fcis-22002	160	29	a	a	DET
fcis-22002	160	30	,	,	PUNCT
fcis-22002	160	31	b	b	NOUN
fcis-22002	160	32	,	,	PUNCT
fcis-22002	160	33	and	and	CCONJ
fcis-22002	160	34	c	c	NOUN
fcis-22002	160	35	served	serve	VERB
fcis-22002	160	36	as	as	ADP
fcis-22002	160	37	a	a	DET
fcis-22002	160	38	training	training	NOUN
fcis-22002	160	39	set	set	NOUN
fcis-22002	160	40	containing	contain	VERB
fcis-22002	160	41	30	30	NUM
fcis-22002	160	42	speakers	speaker	NOUN
fcis-22002	160	43	,	,	PUNCT
fcis-22002	160	44	while	while	SCONJ
fcis-22002	160	45	group	group	NOUN
fcis-22002	160	46	d	d	PROPN
fcis-22002	160	47	served	serve	VERB
fcis-22002	160	48	as	as	ADP
fcis-22002	160	49	a	a	DET
fcis-22002	160	50	test	test	NOUN
fcis-22002	160	51	set	set	NOUN
fcis-22002	160	52	containing	contain	VERB
fcis-22002	160	53	10	10	NUM
fcis-22002	160	54	speakers	speaker	NOUN
fcis-22002	160	55	.	.	PUNCT
fcis-22002	161	1	speech	speech	NOUN
fcis-22002	161	2	files	file	NOUN
fcis-22002	161	3	with	with	ADP
fcis-22002	161	4	the	the	DET
fcis-22002	161	5	same	same	ADJ
fcis-22002	161	6	i	i	NOUN
fcis-22002	161	7	d	d	PROPN
fcis-22002	161	8	in	in	ADP
fcis-22002	161	9	each	each	DET
fcis-22002	161	10	group	group	NOUN
fcis-22002	161	11	have	have	VERB
fcis-22002	161	12	the	the	DET
fcis-22002	161	13	same	same	ADJ
fcis-22002	161	14	content	content	NOUN
fcis-22002	161	15	,	,	PUNCT
fcis-22002	161	16	each	each	DET
fcis-22002	161	17	speech	speech	NOUN
fcis-22002	161	18	has	have	VERB
fcis-22002	161	19	a	a	DET
fcis-22002	161	20	different	different	ADJ
fcis-22002	161	21	duration	duration	NOUN
fcis-22002	161	22	,	,	PUNCT
fcis-22002	161	23	and	and	CCONJ
fcis-22002	161	24	each	each	DET
fcis-22002	161	25	speech	speech	NOUN
fcis-22002	161	26	has	have	AUX
fcis-22002	161	27	not	not	PART
fcis-22002	161	28	only	only	ADV
fcis-22002	161	29	text	text	NOUN
fcis-22002	161	30	labels	label	NOUN
fcis-22002	161	31	but	but	CCONJ
fcis-22002	161	32	also	also	ADV
fcis-22002	161	33	phoneme	phoneme	VERB
fcis-22002	161	34	labels	label	NOUN
fcis-22002	161	35	.	.	PUNCT
fcis-22002	162	1	we	we	PRON
fcis-22002	162	2	selected	select	VERB
fcis-22002	162	3	6000	6000	NUM
fcis-22002	162	4	voices	voice	NOUN
fcis-22002	162	5	from	from	ADP
fcis-22002	162	6	group	group	NOUN
fcis-22002	162	7	abc	abc	PROPN
fcis-22002	162	8	as	as	ADP
fcis-22002	162	9	the	the	DET
fcis-22002	162	10	training	training	NOUN
fcis-22002	162	11	set	set	NOUN
fcis-22002	162	12	,	,	PUNCT
fcis-22002	162	13	and	and	CCONJ
fcis-22002	162	14	1000	1000	NUM
fcis-22002	162	15	voices	voice	NOUN
fcis-22002	162	16	from	from	ADP
fcis-22002	162	17	group	group	PROPN
fcis-22002	162	18	d	d	PROPN
fcis-22002	162	19	with	with	ADP
fcis-22002	162	20	100	100	NUM
fcis-22002	162	21	different	different	ADJ
fcis-22002	162	22	contents	content	NOUN
fcis-22002	162	23	as	as	ADP
fcis-22002	162	24	the	the	DET
fcis-22002	162	25	test	test	NOUN
fcis-22002	162	26	set	set	VERB
fcis-22002	162	27	.	.	PUNCT
fcis-22002	163	1	the	the	DET
fcis-22002	163	2	labels	label	NOUN
fcis-22002	163	3	are	be	AUX
fcis-22002	163	4	the	the	DET
fcis-22002	163	5	phoneme	phoneme	ADJ
fcis-22002	163	6	labels	label	NOUN
fcis-22002	163	7	of	of	ADP
fcis-22002	163	8	each	each	DET
fcis-22002	163	9	speech	speech	NOUN
fcis-22002	163	10	in	in	ADP
fcis-22002	163	11	the	the	DET
fcis-22002	163	12	dataset	dataset	NOUN
fcis-22002	163	13	for	for	ADP
fcis-22002	163	14	pretraining	pretraine	VERB
fcis-22002	163	15	,	,	PUNCT
fcis-22002	163	16	and	and	CCONJ
fcis-22002	163	17	the	the	DET
fcis-22002	163	18	classification	classification	NOUN
fcis-22002	163	19	labels	label	NOUN
fcis-22002	163	20	(	(	PUNCT
fcis-22002	163	21	one	one	NUM
fcis-22002	163	22	-	-	PUNCT
fcis-22002	163	23	hot	hot	ADJ
fcis-22002	163	24	codes	code	NOUN
fcis-22002	163	25	)	)	PUNCT
fcis-22002	163	26	generated	generate	VERB
fcis-22002	163	27	from	from	ADP
fcis-22002	163	28	the	the	DET
fcis-22002	163	29	textual	textual	ADJ
fcis-22002	163	30	content	content	NOUN
fcis-22002	163	31	of	of	ADP
fcis-22002	163	32	the	the	DET
fcis-22002	163	33	speech	speech	NOUN
fcis-22002	163	34	for	for	ADP
fcis-22002	163	35	training	training	NOUN
fcis-22002	163	36	.	.	PUNCT
fcis-22002	164	1	4.1.2	4.1.2	X
fcis-22002	164	2	.	.	PUNCT
fcis-22002	164	3	model	model	NOUN
fcis-22002	164	4	setup	setup	NOUN
fcis-22002	164	5	1	1	NUM
fcis-22002	164	6	.	.	PUNCT
fcis-22002	164	7	encoder	encoder	NOUN
fcis-22002	164	8	:	:	PUNCT
fcis-22002	164	9	15	15	NUM
fcis-22002	164	10	wavenet	wavenet	ADJ
fcis-22002	164	11	residual	residual	ADJ
fcis-22002	164	12	blocks	block	NOUN
fcis-22002	164	13	were	be	AUX
fcis-22002	164	14	used	use	VERB
fcis-22002	164	15	to	to	PART
fcis-22002	164	16	stack	stack	VERB
fcis-22002	164	17	the	the	DET
fcis-22002	164	18	wavenet	wavenet	ADJ
fcis-22002	164	19	residual	residual	ADJ
fcis-22002	164	20	blocks	block	NOUN
fcis-22002	164	21	with	with	ADP
fcis-22002	164	22	causal	causal	ADJ
fcis-22002	164	23	null	null	ADJ
fcis-22002	164	24	convolution	convolution	NOUN
fcis-22002	164	25	having	have	VERB
fcis-22002	164	26	128	128	NUM
fcis-22002	164	27	filters	filter	NOUN
fcis-22002	164	28	of	of	ADP
fcis-22002	164	29	filter	filter	NOUN
fcis-22002	164	30	size	size	NOUN
fcis-22002	164	31	7	7	NUM
fcis-22002	164	32	and	and	CCONJ
fcis-22002	164	33	null	null	ADJ
fcis-22002	164	34	rates	rate	NOUN
fcis-22002	164	35	of	of	ADP
fcis-22002	164	36	1	1	NUM
fcis-22002	164	37	,	,	PUNCT
fcis-22002	164	38	2	2	NUM
fcis-22002	164	39	,	,	PUNCT
fcis-22002	164	40	4	4	NUM
fcis-22002	164	41	,	,	PUNCT
fcis-22002	164	42	8	8	NUM
fcis-22002	164	43	and	and	CCONJ
fcis-22002	164	44	16	16	NUM
fcis-22002	164	45	looped	loop	VERB
fcis-22002	164	46	three	three	NUM
fcis-22002	164	47	times	time	NOUN
fcis-22002	164	48	from	from	ADP
fcis-22002	164	49	input	input	NOUN
fcis-22002	164	50	to	to	ADP
fcis-22002	164	51	output	output	NOUN
fcis-22002	164	52	.	.	PUNCT
fcis-22002	165	1	the	the	DET
fcis-22002	165	2	input	input	NOUN
fcis-22002	165	3	data	datum	NOUN
fcis-22002	165	4	is	be	AUX
fcis-22002	165	5	processed	process	VERB
fcis-22002	165	6	simultaneously	simultaneously	ADV
fcis-22002	165	7	in	in	ADP
fcis-22002	165	8	the	the	DET
fcis-22002	165	9	residual	residual	ADJ
fcis-22002	165	10	block	block	NOUN
fcis-22002	165	11	by	by	ADP
fcis-22002	165	12	two	two	NUM
fcis-22002	165	13	convolutional	convolutional	ADJ
fcis-22002	165	14	layers	layer	NOUN
fcis-22002	165	15	,	,	PUNCT
fcis-22002	165	16	one	one	NUM
fcis-22002	165	17	of	of	ADP
fcis-22002	165	18	which	which	PRON
fcis-22002	165	19	uses	use	VERB
fcis-22002	165	20	a	a	DET
fcis-22002	165	21	tanh	tanh	NOUN
fcis-22002	165	22	activation	activation	NOUN
fcis-22002	165	23	function	function	NOUN
fcis-22002	165	24	and	and	CCONJ
fcis-22002	165	25	the	the	DET
fcis-22002	165	26	other	other	ADJ
fcis-22002	165	27	uses	use	VERB
fcis-22002	165	28	a	a	DET
fcis-22002	165	29	sigmoid	sigmoid	NOUN
fcis-22002	165	30	activation	activation	NOUN
fcis-22002	165	31	function	function	NOUN
fcis-22002	165	32	,	,	PUNCT
fcis-22002	165	33	and	and	CCONJ
fcis-22002	165	34	the	the	DET
fcis-22002	165	35	outputs	output	NOUN
fcis-22002	165	36	of	of	ADP
fcis-22002	165	37	these	these	DET
fcis-22002	165	38	two	two	NUM
fcis-22002	165	39	convolutional	convolutional	ADJ
fcis-22002	165	40	layers	layer	NOUN
fcis-22002	165	41	are	be	AUX
fcis-22002	165	42	combined	combine	VERB
fcis-22002	165	43	by	by	ADP
fcis-22002	165	44	element	element	NOUN
fcis-22002	165	45	-	-	PUNCT
fcis-22002	165	46	by	by	ADP
fcis-22002	165	47	-	-	PUNCT
fcis-22002	165	48	element	element	NOUN
fcis-22002	165	49	multiplication	multiplication	NOUN
fcis-22002	165	50	.	.	PUNCT
fcis-22002	166	1	this	this	PRON
fcis-22002	166	2	is	be	AUX
fcis-22002	166	3	the	the	DET
fcis-22002	166	4	gated	gate	VERB
fcis-22002	166	5	linear	linear	NOUN
fcis-22002	166	6	unit	unit	NOUN
fcis-22002	166	7	,	,	PUNCT
fcis-22002	166	8	the	the	DET
fcis-22002	166	9	output	output	NOUN
fcis-22002	166	10	of	of	ADP
fcis-22002	166	11	the	the	DET
fcis-22002	166	12	gated	gate	VERB
fcis-22002	166	13	linear	linear	ADJ
fcis-22002	166	14	unit	unit	NOUN
fcis-22002	166	15	is	be	AUX
fcis-22002	166	16	processed	process	VERB
fcis-22002	166	17	by	by	ADP
fcis-22002	166	18	both	both	CCONJ
fcis-22002	166	19	the	the	DET
fcis-22002	166	20	convolutional	convolutional	ADJ
fcis-22002	166	21	layers	layer	NOUN
fcis-22002	166	22	,	,	PUNCT
fcis-22002	166	23	the	the	DET
fcis-22002	166	24	tanh	tanh	NOUN
fcis-22002	166	25	activation	activation	NOUN
fcis-22002	166	26	function	function	NOUN
fcis-22002	166	27	,	,	PUNCT
fcis-22002	166	28	the	the	DET
fcis-22002	166	29	output	output	NOUN
fcis-22002	166	30	of	of	ADP
fcis-22002	166	31	one	one	NUM
fcis-22002	166	32	of	of	ADP
fcis-22002	166	33	them	they	PRON
fcis-22002	166	34	is	be	AUX
fcis-22002	166	35	used	use	VERB
fcis-22002	166	36	to	to	PART
fcis-22002	166	37	pass	pass	VERB
fcis-22002	166	38	on	on	ADP
fcis-22002	166	39	to	to	ADP
fcis-22002	166	40	the	the	DET
fcis-22002	166	41	next	next	ADJ
fcis-22002	166	42	residual	residual	ADJ
fcis-22002	166	43	block	block	NOUN
fcis-22002	166	44	and	and	CCONJ
fcis-22002	166	45	the	the	DET
fcis-22002	166	46	output	output	NOUN
fcis-22002	166	47	of	of	ADP
fcis-22002	166	48	the	the	DET
fcis-22002	166	49	other	other	ADJ
fcis-22002	166	50	one	one	NOUN
fcis-22002	166	51	is	be	AUX
fcis-22002	166	52	output	output	NOUN
fcis-22002	166	53	after	after	ADP
fcis-22002	166	54	connecting	connect	VERB
fcis-22002	166	55	it	it	PRON
fcis-22002	166	56	with	with	ADP
fcis-22002	166	57	the	the	DET
fcis-22002	166	58	input	input	NOUN
fcis-22002	166	59	residuals	residual	NOUN
fcis-22002	166	60	and	and	CCONJ
fcis-22002	166	61	is	be	AUX
fcis-22002	166	62	used	use	VERB
fcis-22002	166	63	for	for	ADP
fcis-22002	166	64	the	the	DET
fcis-22002	166	65	subsequent	subsequent	ADJ
fcis-22002	166	66	residuals	residual	NOUN
fcis-22002	166	67	to	to	PART
fcis-22002	166	68	be	be	AUX
fcis-22002	166	69	connected	connect	VERB
fcis-22002	166	70	.	.	PUNCT
fcis-22002	167	1	the	the	DET
fcis-22002	167	2	encoder	encoder	NOUN
fcis-22002	167	3	will	will	AUX
fcis-22002	167	4	sum	sum	VERB
fcis-22002	167	5	all	all	DET
fcis-22002	167	6	the	the	DET
fcis-22002	167	7	residual	residual	ADJ
fcis-22002	167	8	block	block	NOUN
fcis-22002	167	9	outputs	output	NOUN
fcis-22002	167	10	by	by	ADP
fcis-22002	167	11	element	element	NOUN
fcis-22002	167	12	to	to	ADP
fcis-22002	167	13	encoder	encoder	NOUN
fcis-22002	167	14	output	output	NOUN
fcis-22002	167	15	.	.	PUNCT
fcis-22002	168	1	2	2	X
fcis-22002	168	2	.	.	X
fcis-22002	168	3	downsampling	downsample	VERB
fcis-22002	168	4	module	module	NOUN
fcis-22002	168	5	:	:	PUNCT
fcis-22002	168	6	the	the	DET
fcis-22002	168	7	entire	entire	ADJ
fcis-22002	168	8	downsampling	downsample	VERB
fcis-22002	168	9	module	module	NOUN
fcis-22002	168	10	contains	contain	VERB
fcis-22002	168	11	five	five	NUM
fcis-22002	168	12	blocks	block	NOUN
fcis-22002	168	13	,	,	PUNCT
fcis-22002	168	14	each	each	PRON
fcis-22002	168	15	of	of	ADP
fcis-22002	168	16	which	which	PRON
fcis-22002	168	17	includes	include	VERB
fcis-22002	168	18	causal	causal	ADJ
fcis-22002	168	19	convolution	convolution	NOUN
fcis-22002	168	20	,	,	PUNCT
fcis-22002	168	21	batch	batch	NOUN
fcis-22002	168	22	normalization	normalization	NOUN
fcis-22002	168	23	,	,	PUNCT
fcis-22002	168	24	relu	relu	NOUN
fcis-22002	168	25	activation	activation	NOUN
fcis-22002	168	26	function	function	NOUN
fcis-22002	168	27	,	,	PUNCT
fcis-22002	168	28	and	and	CCONJ
fcis-22002	168	29	maximum	maximum	ADJ
fcis-22002	168	30	pooling	pooling	NOUN
fcis-22002	168	31	.	.	PUNCT
fcis-22002	169	1	the	the	DET
fcis-22002	169	2	filter	filter	NOUN
fcis-22002	169	3	size	size	NOUN
fcis-22002	169	4	of	of	ADP
fcis-22002	169	5	the	the	DET
fcis-22002	169	6	convolution	convolution	NOUN
fcis-22002	169	7	layer	layer	NOUN
fcis-22002	169	8	is	be	AUX
fcis-22002	169	9	15	15	NUM
fcis-22002	169	10	and	and	CCONJ
fcis-22002	169	11	the	the	DET
fcis-22002	169	12	number	number	NOUN
fcis-22002	169	13	of	of	ADP
fcis-22002	169	14	filters	filter	NOUN
fcis-22002	169	15	is	be	AUX
fcis-22002	169	16	(	(	PUNCT
fcis-22002	169	17	l+1	l+1	PROPN
fcis-22002	169	18	)	)	PUNCT
fcis-22002	169	19	×	×	NOUN
fcis-22002	169	20	128	128	NUM
fcis-22002	169	21	,	,	PUNCT
fcis-22002	169	22	where	where	SCONJ
fcis-22002	169	23	l	l	NOUN
fcis-22002	169	24	represents	represent	VERB
fcis-22002	169	25	the	the	DET
fcis-22002	169	26	layer	layer	NOUN
fcis-22002	169	27	number	number	NOUN
fcis-22002	169	28	.	.	PUNCT
fcis-22002	170	1	the	the	DET
fcis-22002	170	2	pooling	pool	VERB
fcis-22002	170	3	layer	layer	NOUN
fcis-22002	170	4	has	have	VERB
fcis-22002	170	5	a	a	DET
fcis-22002	170	6	width	width	NOUN
fcis-22002	170	7	of	of	ADP
fcis-22002	170	8	2	2	NUM
fcis-22002	170	9	,	,	PUNCT
fcis-22002	170	10	making	make	VERB
fcis-22002	170	11	the	the	DET
fcis-22002	170	12	data	datum	NOUN
fcis-22002	170	13	time	time	NOUN
fcis-22002	170	14	step	step	NOUN
fcis-22002	170	15	for	for	ADP
fcis-22002	170	16	each	each	DET
fcis-22002	170	17	layer	layer	NOUN
fcis-22002	170	18	halved	halve	VERB
fcis-22002	170	19	.	.	PUNCT
fcis-22002	171	1	3	3	X
fcis-22002	171	2	.	.	X
fcis-22002	171	3	decoder	decoder	NOUN
fcis-22002	171	4	:	:	PUNCT
fcis-22002	171	5	the	the	DET
fcis-22002	171	6	positional	positional	ADJ
fcis-22002	171	7	encoding	encoding	NOUN
fcis-22002	171	8	is	be	AUX
fcis-22002	171	9	added	add	VERB
fcis-22002	171	10	before	before	SCONJ
fcis-22002	171	11	the	the	DET
fcis-22002	171	12	data	data	NOUN
fcis-22002	171	13	enters	enter	VERB
fcis-22002	171	14	the	the	DET
fcis-22002	171	15	decoder	decoder	NOUN
fcis-22002	171	16	.	.	PUNCT
fcis-22002	172	1	the	the	DET
fcis-22002	172	2	number	number	NOUN
fcis-22002	172	3	of	of	ADP
fcis-22002	172	4	headers	header	NOUN
fcis-22002	172	5	in	in	ADP
fcis-22002	172	6	the	the	DET
fcis-22002	172	7	twolayer	twolayer	NOUN
fcis-22002	172	8	transformer	transformer	NOUN
fcis-22002	172	9	block	block	NOUN
fcis-22002	172	10	structure	structure	NOUN
fcis-22002	172	11	after	after	SCONJ
fcis-22002	172	12	the	the	DET
fcis-22002	172	13	decoder	decoder	NOUN
fcis-22002	172	14	is	be	AUX
fcis-22002	172	15	set	set	VERB
fcis-22002	172	16	to	to	ADP
fcis-22002	172	17	24	24	NUM
fcis-22002	172	18	.	.	PUNCT
fcis-22002	173	1	the	the	DET
fcis-22002	173	2	output	output	NOUN
fcis-22002	173	3	of	of	ADP
fcis-22002	173	4	the	the	DET
fcis-22002	173	5	decoder	decoder	NOUN
fcis-22002	173	6	is	be	AUX
fcis-22002	173	7	obtained	obtain	VERB
fcis-22002	173	8	from	from	ADP
fcis-22002	173	9	the	the	DET
fcis-22002	173	10	final	final	ADJ
fcis-22002	173	11	hidden	hidden	ADJ
fcis-22002	173	12	state	state	NOUN
fcis-22002	173	13	of	of	ADP
fcis-22002	173	14	the	the	DET
fcis-22002	173	15	token	token	NOUN
fcis-22002	173	16	in	in	ADP
fcis-22002	173	17	the	the	DET
fcis-22002	173	18	data	datum	NOUN
fcis-22002	173	19	through	through	ADP
fcis-22002	173	20	the	the	DET
fcis-22002	173	21	tanh	tanh	PROPN
fcis-22002	173	22	activation	activation	NOUN
fcis-22002	173	23	function	function	NOUN
fcis-22002	173	24	.	.	PUNCT
fcis-22002	174	1	4.1.3	4.1.3	X
fcis-22002	174	2	.	.	PUNCT
fcis-22002	174	3	training	training	NOUN
fcis-22002	174	4	environment	environment	NOUN
fcis-22002	174	5	setup	setup	NOUN
fcis-22002	174	6	pre	pre	VERB
fcis-22002	174	7	-	-	NOUN
fcis-22002	174	8	training	training	NOUN
fcis-22002	174	9	is	be	AUX
fcis-22002	174	10	done	do	VERB
fcis-22002	174	11	using	use	VERB
fcis-22002	174	12	sgd	sgd	PROPN
fcis-22002	174	13	(	(	PUNCT
fcis-22002	174	14	stochastic	stochastic	ADJ
fcis-22002	174	15	gradient	gradient	ADJ
fcis-22002	174	16	descent	descent	NOUN
fcis-22002	174	17	)	)	PUNCT
fcis-22002	174	18	optimizer	optimizer	NOUN
fcis-22002	174	19	,	,	PUNCT
fcis-22002	174	20	the	the	DET
fcis-22002	174	21	learning	learning	NOUN
fcis-22002	174	22	rate	rate	NOUN
fcis-22002	174	23	is	be	AUX
fcis-22002	174	24	initially	initially	ADV
fcis-22002	174	25	0.01	0.01	NUM
fcis-22002	174	26	,	,	PUNCT
fcis-22002	174	27	the	the	DET
fcis-22002	174	28	data	data	NOUN
fcis-22002	174	29	is	be	AUX
fcis-22002	174	30	divided	divide	VERB
fcis-22002	174	31	into	into	ADP
fcis-22002	174	32	training	training	NOUN
fcis-22002	174	33	set	set	NOUN
fcis-22002	174	34	and	and	CCONJ
fcis-22002	174	35	validation	validation	NOUN
fcis-22002	174	36	set	set	VERB
fcis-22002	174	37	by	by	ADP
fcis-22002	174	38	9:1	9:1	NUM
fcis-22002	174	39	,	,	PUNCT
fcis-22002	174	40	the	the	DET
fcis-22002	174	41	learning	learning	NOUN
fcis-22002	174	42	rate	rate	NOUN
fcis-22002	174	43	decreases	decrease	VERB
fcis-22002	174	44	to	to	ADP
fcis-22002	174	45	20	20	NUM
fcis-22002	174	46	%	%	NOUN
fcis-22002	174	47	of	of	ADP
fcis-22002	174	48	the	the	DET
fcis-22002	174	49	original	original	NOUN
fcis-22002	174	50	in	in	ADP
fcis-22002	174	51	the	the	DET
fcis-22002	174	52	next	next	ADJ
fcis-22002	174	53	epoch	epoch	NOUN
fcis-22002	174	54	when	when	SCONJ
fcis-22002	174	55	the	the	DET
fcis-22002	174	56	loss	loss	NOUN
fcis-22002	174	57	value	value	NOUN
fcis-22002	174	58	does	do	AUX
fcis-22002	174	59	n't	not	PART
fcis-22002	174	60	decrease	decrease	VERB
fcis-22002	174	61	,	,	PUNCT
fcis-22002	174	62	and	and	CCONJ
fcis-22002	174	63	the	the	DET
fcis-22002	174	64	early	early	ADJ
fcis-22002	174	65	stopping	stopping	NOUN
fcis-22002	174	66	condition	condition	NOUN
fcis-22002	174	67	is	be	AUX
fcis-22002	174	68	set	set	VERB
fcis-22002	174	69	to	to	ADP
fcis-22002	174	70	the	the	DET
fcis-22002	174	71	validation	validation	NOUN
fcis-22002	174	72	set	set	VERB
fcis-22002	174	73	loss	loss	NOUN
fcis-22002	174	74	of	of	ADP
fcis-22002	174	75	5	5	NUM
fcis-22002	174	76	epochs	epoch	NOUN
fcis-22002	174	77	without	without	ADP
fcis-22002	174	78	decreasing	decrease	VERB
fcis-22002	174	79	.	.	PUNCT
fcis-22002	175	1	the	the	DET
fcis-22002	175	2	encoder	encoder	NOUN
fcis-22002	175	3	weights	weight	VERB
fcis-22002	175	4	for	for	ADP
fcis-22002	175	5	classification	classification	NOUN
fcis-22002	175	6	training	training	NOUN
fcis-22002	175	7	are	be	AUX
fcis-22002	175	8	derived	derive	VERB
fcis-22002	175	9	from	from	ADP
fcis-22002	175	10	the	the	DET
fcis-22002	175	11	pre	pre	NOUN
fcis-22002	175	12	-	-	NOUN
fcis-22002	175	13	training	training	NOUN
fcis-22002	175	14	,	,	PUNCT
fcis-22002	175	15	while	while	SCONJ
fcis-22002	175	16	the	the	DET
fcis-22002	175	17	other	other	ADJ
fcis-22002	175	18	parts	part	NOUN
fcis-22002	175	19	of	of	ADP
fcis-22002	175	20	the	the	DET
fcis-22002	175	21	initial	initial	ADJ
fcis-22002	175	22	weights	weight	NOUN
fcis-22002	175	23	are	be	AUX
fcis-22002	175	24	randomly	randomly	ADV
fcis-22002	175	25	set	set	VERB
fcis-22002	175	26	between	between	ADP
fcis-22002	175	27	-0.05	-0.05	NUM
fcis-22002	175	28	and	and	CCONJ
fcis-22002	175	29	0.05	0.05	NUM
fcis-22002	175	30	.	.	PUNCT
fcis-22002	176	1	the	the	DET
fcis-22002	176	2	model	model	NOUN
fcis-22002	176	3	is	be	AUX
fcis-22002	176	4	trained	train	VERB
fcis-22002	176	5	using	use	VERB
fcis-22002	176	6	adam	adam	PROPN
fcis-22002	176	7	optimizer	optimizer	NOUN
fcis-22002	176	8	with	with	ADP
fcis-22002	176	9	a	a	DET
fcis-22002	176	10	learning	learn	VERB
fcis-22002	176	11	rate	rate	NOUN
fcis-22002	176	12	of	of	ADP
fcis-22002	176	13	0.001	0.001	NUM
fcis-22002	176	14	.	.	PUNCT
fcis-22002	177	1	all	all	DET
fcis-22002	177	2	experimental	experimental	ADJ
fcis-22002	177	3	results	result	NOUN
fcis-22002	177	4	are	be	AUX
fcis-22002	177	5	based	base	VERB
fcis-22002	177	6	on	on	ADP
fcis-22002	177	7	50	50	NUM
fcis-22002	177	8	training	training	NOUN
fcis-22002	177	9	cycles	cycle	NOUN
fcis-22002	177	10	and	and	CCONJ
fcis-22002	177	11	the	the	DET
fcis-22002	177	12	training	training	NOUN
fcis-22002	177	13	data	datum	NOUN
fcis-22002	177	14	is	be	AUX
fcis-22002	177	15	divided	divide	VERB
fcis-22002	177	16	into	into	ADP
fcis-22002	177	17	training	training	NOUN
fcis-22002	177	18	and	and	CCONJ
fcis-22002	177	19	validation	validation	NOUN
fcis-22002	177	20	sets	set	NOUN
fcis-22002	177	21	in	in	ADP
fcis-22002	177	22	a	a	DET
fcis-22002	177	23	ratio	ratio	NOUN
fcis-22002	177	24	of	of	ADP
fcis-22002	177	25	80:20	80:20	NUM
fcis-22002	177	26	.	.	PUNCT
fcis-22002	178	1	the	the	DET
fcis-22002	178	2	platform	platform	NOUN
fcis-22002	178	3	configuration	configuration	NOUN
fcis-22002	178	4	used	use	VERB
fcis-22002	178	5	for	for	ADP
fcis-22002	178	6	all	all	DET
fcis-22002	178	7	training	training	NOUN
fcis-22002	178	8	is	be	AUX
fcis-22002	178	9	:	:	PUNCT
fcis-22002	178	10	intel	intel	PROPN
fcis-22002	178	11	core	core	PROPN
fcis-22002	178	12	i5	i5	PROPN
fcis-22002	178	13	-	-	PUNCT
fcis-22002	178	14	12400	12400	NUM
fcis-22002	178	15	,	,	PUNCT
fcis-22002	178	16	rtx3060	rtx3060	VERB
fcis-22002	178	17	12	12	NUM
fcis-22002	178	18	g	g	NOUN
fcis-22002	178	19	,	,	PUNCT
fcis-22002	178	20	python	python	NOUN
fcis-22002	178	21	3.9	3.9	NUM
fcis-22002	178	22	,	,	PUNCT
fcis-22002	178	23	tensorflow	tensorflow	NOUN
fcis-22002	178	24	2.6.0	2.6.0	NUM
fcis-22002	178	25	.	.	PROPN
fcis-22002	178	26	4.2	4.2	NUM
fcis-22002	178	27	.	.	PUNCT
fcis-22002	178	28	comparison	comparison	NOUN
fcis-22002	178	29	with	with	ADP
fcis-22002	178	30	traditional	traditional	ADJ
fcis-22002	178	31	models	model	NOUN
fcis-22002	178	32	in	in	ADP
fcis-22002	178	33	order	order	NOUN
fcis-22002	178	34	to	to	PART
fcis-22002	178	35	show	show	VERB
fcis-22002	178	36	that	that	SCONJ
fcis-22002	178	37	the	the	DET
fcis-22002	178	38	hybrid	hybrid	ADJ
fcis-22002	178	39	model	model	NOUN
fcis-22002	178	40	in	in	ADP
fcis-22002	178	41	this	this	DET
fcis-22002	178	42	paper	paper	NOUN
fcis-22002	178	43	has	have	VERB
fcis-22002	178	44	a	a	DET
fcis-22002	178	45	significant	significant	ADJ
fcis-22002	178	46	advantage	advantage	NOUN
fcis-22002	178	47	in	in	ADP
fcis-22002	178	48	end	end	NOUN
fcis-22002	178	49	-	-	PUNCT
fcis-22002	178	50	to	to	ADP
fcis-22002	178	51	-	-	PUNCT
fcis-22002	178	52	end	end	NOUN
fcis-22002	178	53	speech	speech	NOUN
fcis-22002	178	54	content	content	NOUN
fcis-22002	178	55	hash	hash	NOUN
fcis-22002	178	56	matching	matching	NOUN
fcis-22002	178	57	,	,	PUNCT
fcis-22002	178	58	a	a	DET
fcis-22002	178	59	bi	bi	ADJ
fcis-22002	178	60	-	-	ADJ
fcis-22002	178	61	directional	directional	ADJ
fcis-22002	178	62	lstm	lstm	NOUN
fcis-22002	178	63	model	model	NOUN
fcis-22002	178	64	is	be	AUX
fcis-22002	178	65	chosen	choose	VERB
fcis-22002	178	66	as	as	ADP
fcis-22002	178	67	the	the	DET
fcis-22002	178	68	control	control	NOUN
fcis-22002	178	69	group	group	NOUN
fcis-22002	178	70	,	,	PUNCT
fcis-22002	178	71	which	which	PRON
fcis-22002	178	72	has	have	AUX
fcis-22002	178	73	long	long	ADV
fcis-22002	178	74	been	be	AUX
fcis-22002	178	75	popular	popular	ADJ
fcis-22002	178	76	in	in	ADP
fcis-22002	178	77	the	the	DET
fcis-22002	178	78	field	field	NOUN
fcis-22002	178	79	of	of	ADP
fcis-22002	178	80	speech	speech	NOUN
fcis-22002	178	81	recognition	recognition	NOUN
fcis-22002	178	82	and	and	CCONJ
fcis-22002	178	83	also	also	ADV
fcis-22002	178	84	used	use	VERB
fcis-22002	178	85	in	in	ADP
fcis-22002	178	86	the	the	DET
fcis-22002	178	87	task	task	NOUN
fcis-22002	178	88	of	of	ADP
fcis-22002	178	89	speech	speech	NOUN
fcis-22002	178	90	hash	hash	NOUN
fcis-22002	178	91	extraction	extraction	NOUN
fcis-22002	178	92	.	.	PUNCT
fcis-22002	179	1	the	the	DET
fcis-22002	179	2	control	control	NOUN
fcis-22002	179	3	model	model	NOUN
fcis-22002	179	4	has	have	VERB
fcis-22002	179	5	the	the	DET
fcis-22002	179	6	same	same	ADJ
fcis-22002	179	7	amount	amount	NOUN
fcis-22002	179	8	of	of	ADP
fcis-22002	179	9	data	datum	NOUN
fcis-22002	179	10	as	as	ADP
fcis-22002	179	11	the	the	DET
fcis-22002	179	12	model	model	NOUN
fcis-22002	179	13	in	in	ADP
fcis-22002	179	14	this	this	DET
fcis-22002	179	15	paper	paper	NOUN
fcis-22002	179	16	.	.	PUNCT
fcis-22002	180	1	the	the	DET
fcis-22002	180	2	specific	specific	ADJ
fcis-22002	180	3	control	control	NOUN
fcis-22002	180	4	data	datum	NOUN
fcis-22002	180	5	is	be	AUX
fcis-22002	180	6	shown	show	VERB
fcis-22002	180	7	in	in	ADP
fcis-22002	180	8	table	table	NOUN
fcis-22002	180	9	1	1	NUM
fcis-22002	180	10	.	.	PUNCT
fcis-22002	180	11	table	table	NOUN
fcis-22002	180	12	1	1	NUM
fcis-22002	180	13	.	.	PUNCT
fcis-22002	180	14	comparison	comparison	NOUN
fcis-22002	180	15	with	with	ADP
fcis-22002	180	16	baseline	baseline	NOUN
fcis-22002	180	17	model	model	NOUN
fcis-22002	180	18	model	model	PROPN
fcis-22002	180	19	retrieval	retrieval	NOUN
fcis-22002	180	20	of	of	ADP
fcis-22002	180	21	indicators	indicator	NOUN
fcis-22002	180	22	map	map	VERB
fcis-22002	180	23	mrr	mrr	PROPN
fcis-22002	180	24	p@1	p@1	PROPN
fcis-22002	180	25	p@5	p@5	PROPN
fcis-22002	180	26	p@9	p@9	PROPN
fcis-22002	180	27	this	this	DET
fcis-22002	180	28	method	method	NOUN
fcis-22002	180	29	0.912	0.912	NUM
fcis-22002	181	1	0.984	0.984	NUM
fcis-22002	181	2	0.993	0.993	NUM
fcis-22002	181	3	0.981	0.981	NUM
fcis-22002	181	4	0.919	0.919	NUM
fcis-22002	181	5	bi	bi	NOUN
fcis-22002	181	6	-	-	ADJ
fcis-22002	181	7	lstm	lstm	ADJ
fcis-22002	181	8	0.310	0.310	NUM
fcis-22002	181	9	0.497	0.497	NUM
fcis-22002	181	10	0.645	0.645	NUM
fcis-22002	181	11	0.488	0.488	NUM
fcis-22002	181	12	0.380	0.380	NUM
fcis-22002	181	13	based	base	VERB
fcis-22002	181	14	on	on	ADP
fcis-22002	181	15	the	the	DET
fcis-22002	181	16	above	above	ADJ
fcis-22002	181	17	table	table	NOUN
fcis-22002	181	18	,	,	PUNCT
fcis-22002	181	19	it	it	PRON
fcis-22002	181	20	can	can	AUX
fcis-22002	181	21	be	be	AUX
fcis-22002	181	22	seen	see	VERB
fcis-22002	181	23	that	that	SCONJ
fcis-22002	181	24	there	there	PRON
fcis-22002	181	25	is	be	VERB
fcis-22002	181	26	a	a	DET
fcis-22002	181	27	huge	huge	ADJ
fcis-22002	181	28	gap	gap	NOUN
fcis-22002	181	29	between	between	ADP
fcis-22002	181	30	the	the	DET
fcis-22002	181	31	traditional	traditional	ADJ
fcis-22002	181	32	model	model	NOUN
fcis-22002	181	33	applied	apply	VERB
fcis-22002	181	34	directly	directly	ADV
fcis-22002	181	35	on	on	ADP
fcis-22002	181	36	this	this	DET
fcis-22002	181	37	task	task	NOUN
fcis-22002	181	38	and	and	CCONJ
fcis-22002	181	39	the	the	DET
fcis-22002	181	40	hybrid	hybrid	ADJ
fcis-22002	181	41	model	model	NOUN
fcis-22002	181	42	of	of	ADP
fcis-22002	181	43	this	this	DET
fcis-22002	181	44	study	study	NOUN
fcis-22002	181	45	,	,	PUNCT
fcis-22002	181	46	which	which	PRON
fcis-22002	181	47	proves	prove	VERB
fcis-22002	181	48	the	the	DET
fcis-22002	181	49	superior	superior	ADJ
fcis-22002	181	50	performance	performance	NOUN
fcis-22002	181	51	of	of	ADP
fcis-22002	181	52	the	the	DET
fcis-22002	181	53	hybrid	hybrid	ADJ
fcis-22002	181	54	model	model	NOUN
fcis-22002	181	55	of	of	ADP
fcis-22002	181	56	this	this	DET
fcis-22002	181	57	study	study	NOUN
fcis-22002	181	58	on	on	ADP
fcis-22002	181	59	this	this	DET
fcis-22002	181	60	task	task	NOUN
fcis-22002	181	61	.	.	PUNCT
fcis-22002	182	1	4.3	4.3	NUM
fcis-22002	182	2	.	.	PUNCT
fcis-22002	183	1	the	the	DET
fcis-22002	183	2	effect	effect	NOUN
fcis-22002	183	3	of	of	ADP
fcis-22002	183	4	noise	noise	NOUN
fcis-22002	183	5	interference	interference	NOUN
fcis-22002	183	6	on	on	ADP
fcis-22002	183	7	speech	speech	NOUN
fcis-22002	183	8	retrieval	retrieval	NOUN
fcis-22002	183	9	in	in	ADP
fcis-22002	183	10	practical	practical	ADJ
fcis-22002	183	11	situations	situation	NOUN
fcis-22002	183	12	,	,	PUNCT
fcis-22002	183	13	speech	speech	NOUN
fcis-22002	183	14	retrieval	retrieval	NOUN
fcis-22002	183	15	tasks	task	NOUN
fcis-22002	183	16	may	may	AUX
fcis-22002	183	17	be	be	AUX
fcis-22002	183	18	affected	affect	VERB
fcis-22002	183	19	by	by	ADP
fcis-22002	183	20	noise	noise	NOUN
fcis-22002	183	21	interference	interference	NOUN
fcis-22002	183	22	or	or	CCONJ
fcis-22002	183	23	channel	channel	NOUN
fcis-22002	183	24	interference	interference	NOUN
fcis-22002	183	25	,	,	PUNCT
fcis-22002	183	26	which	which	PRON
fcis-22002	183	27	can	can	AUX
fcis-22002	183	28	lead	lead	VERB
fcis-22002	183	29	to	to	ADP
fcis-22002	183	30	erroneous	erroneous	ADJ
fcis-22002	183	31	retrieval	retrieval	NOUN
fcis-22002	183	32	results	result	NOUN
fcis-22002	183	33	.	.	PUNCT
fcis-22002	184	1	therefore	therefore	ADV
fcis-22002	184	2	,	,	PUNCT
fcis-22002	184	3	in	in	ADP
fcis-22002	184	4	this	this	DET
fcis-22002	184	5	paper	paper	NOUN
fcis-22002	184	6	,	,	PUNCT
fcis-22002	184	7	we	we	PRON
fcis-22002	184	8	investigate	investigate	VERB
fcis-22002	184	9	the	the	DET
fcis-22002	184	10	robustness	robustness	NOUN
fcis-22002	184	11	of	of	ADP
fcis-22002	184	12	the	the	DET
fcis-22002	184	13	method	method	NOUN
fcis-22002	184	14	to	to	PART
fcis-22002	184	15	noise	noise	VERB
fcis-22002	184	16	interference	interference	NOUN
fcis-22002	184	17	in	in	ADP
fcis-22002	184	18	speech	speech	NOUN
fcis-22002	184	19	retrieval	retrieval	NOUN
fcis-22002	184	20	tasks	task	NOUN
fcis-22002	184	21	.	.	PUNCT
fcis-22002	185	1	in	in	ADP
fcis-22002	185	2	order	order	NOUN
fcis-22002	185	3	to	to	PART
fcis-22002	185	4	be	be	AUX
fcis-22002	185	5	able	able	ADJ
fcis-22002	185	6	to	to	PART
fcis-22002	185	7	perform	perform	VERB
fcis-22002	185	8	speech	speech	NOUN
fcis-22002	185	9	retrieval	retrieval	NOUN
fcis-22002	185	10	in	in	ADP
fcis-22002	185	11	noisy	noisy	ADJ
fcis-22002	185	12	environments	environment	NOUN
fcis-22002	185	13	,	,	PUNCT
fcis-22002	185	14	we	we	PRON
fcis-22002	185	15	manually	manually	ADV
fcis-22002	185	16	added	add	VERB
fcis-22002	185	17	gaussian	gaussian	ADJ
fcis-22002	185	18	white	white	ADJ
fcis-22002	185	19	noise	noise	NOUN
fcis-22002	185	20	to	to	ADP
fcis-22002	185	21	the	the	DET
fcis-22002	185	22	test	test	NOUN
fcis-22002	185	23	dataset	dataset	VERB
fcis-22002	185	24	and	and	CCONJ
fcis-22002	185	25	constructed	construct	VERB
fcis-22002	185	26	speech	speech	NOUN
fcis-22002	185	27	retrieval	retrieval	NOUN
fcis-22002	185	28	tasks	task	NOUN
fcis-22002	185	29	with	with	ADP
fcis-22002	185	30	different	different	ADJ
fcis-22002	185	31	signal	signal	NOUN
fcis-22002	185	32	-	-	PUNCT
fcis-22002	185	33	to	to	ADP
fcis-22002	185	34	-	-	PUNCT
fcis-22002	185	35	noise	noise	NOUN
fcis-22002	185	36	ratios	ratio	NOUN
fcis-22002	185	37	(	(	PUNCT
fcis-22002	185	38	snrs	snrs	NOUN
fcis-22002	185	39	)	)	PUNCT
fcis-22002	185	40	,	,	PUNCT
fcis-22002	185	41	including	include	VERB
fcis-22002	185	42	30	30	NUM
fcis-22002	185	43	db	db	PROPN
fcis-22002	185	44	,	,	PUNCT
fcis-22002	185	45	25	25	NUM
fcis-22002	185	46	db	db	NOUN
fcis-22002	185	47	,	,	PUNCT
fcis-22002	185	48	20	20	NUM
fcis-22002	185	49	db	db	NOUN
fcis-22002	185	50	,	,	PUNCT
fcis-22002	185	51	15	15	NUM
fcis-22002	185	52	db	db	NOUN
fcis-22002	185	53	,	,	PUNCT
fcis-22002	185	54	and	and	CCONJ
fcis-22002	185	55	10	10	NUM
fcis-22002	185	56	db	db	NOUN
fcis-22002	185	57	.	.	PUNCT
fcis-22002	186	1	in	in	ADP
fcis-22002	186	2	order	order	NOUN
fcis-22002	186	3	to	to	PART
fcis-22002	186	4	simulate	simulate	VERB
fcis-22002	186	5	the	the	DET
fcis-22002	186	6	training	training	NOUN
fcis-22002	186	7	-	-	PUNCT
fcis-22002	186	8	testing	testing	NOUN
fcis-22002	186	9	mismatch	mismatch	NOUN
fcis-22002	186	10	,	,	PUNCT
fcis-22002	186	11	we	we	PRON
fcis-22002	186	12	trained	train	VERB
fcis-22002	186	13	the	the	DET
fcis-22002	186	14	model	model	NOUN
fcis-22002	186	15	by	by	ADP
fcis-22002	186	16	using	use	VERB
fcis-22002	186	17	the	the	DET
fcis-22002	186	18	speech	speech	NOUN
fcis-22002	186	19	files	file	NOUN
fcis-22002	186	20	with	with	ADP
fcis-22002	186	21	no	no	DET
fcis-22002	186	22	added	add	VERB
fcis-22002	186	23	noise	noise	NOUN
fcis-22002	186	24	as	as	ADP
fcis-22002	186	25	the	the	DET
fcis-22002	186	26	training	training	NOUN
fcis-22002	186	27	set	set	NOUN
fcis-22002	186	28	.	.	PUNCT
fcis-22002	187	1	table	table	NOUN
fcis-22002	187	2	2	2	NUM
fcis-22002	187	3	lists	list	VERB
fcis-22002	187	4	the	the	DET
fcis-22002	187	5	speech	speech	NOUN
fcis-22002	187	6	retrieval	retrieval	NOUN
fcis-22002	187	7	performance	performance	NOUN
fcis-22002	187	8	of	of	ADP
fcis-22002	187	9	this	this	DET
fcis-22002	187	10	method	method	NOUN
fcis-22002	187	11	under	under	ADP
fcis-22002	187	12	different	different	ADJ
fcis-22002	187	13	noise	noise	NOUN
fcis-22002	187	14	conditions	condition	NOUN
fcis-22002	187	15	.	.	PUNCT
fcis-22002	188	1	unsurprisingly	unsurprisingly	ADV
fcis-22002	188	2	,	,	PUNCT
fcis-22002	188	3	the	the	DET
fcis-22002	188	4	speech	speech	NOUN
fcis-22002	188	5	retrieval	retrieval	NOUN
fcis-22002	188	6	performance	performance	NOUN
fcis-22002	188	7	gradually	gradually	ADV
fcis-22002	188	8	decreases	decrease	VERB
fcis-22002	188	9	as	as	ADP
fcis-22002	188	10	the	the	DET
fcis-22002	188	11	noise	noise	NOUN
fcis-22002	188	12	increases	increase	NOUN
fcis-22002	188	13	,	,	PUNCT
fcis-22002	188	14	but	but	CCONJ
fcis-22002	188	15	even	even	ADV
fcis-22002	188	16	at	at	ADP
fcis-22002	188	17	10db	10db	ADV
fcis-22002	188	18	,	,	PUNCT
fcis-22002	188	19	the	the	DET
fcis-22002	188	20	present	present	ADJ
fcis-22002	188	21	method	method	NOUN
fcis-22002	188	22	still	still	ADV
fcis-22002	188	23	maintains	maintain	VERB
fcis-22002	188	24	a	a	DET
fcis-22002	188	25	certain	certain	ADJ
fcis-22002	188	26	retrieval	retrieval	NOUN
fcis-22002	188	27	performance	performance	NOUN
fcis-22002	188	28	and	and	CCONJ
fcis-22002	188	29	is	be	AUX
fcis-22002	188	30	able	able	ADJ
fcis-22002	188	31	to	to	PART
fcis-22002	188	32	approach	approach	VERB
fcis-22002	188	33	the	the	DET
fcis-22002	188	34	situation	situation	NOUN
fcis-22002	188	35	when	when	SCONJ
fcis-22002	188	36	no	no	DET
fcis-22002	188	37	noise	noise	NOUN
fcis-22002	188	38	is	be	AUX
fcis-22002	188	39	added	add	VERB
fcis-22002	188	40	at	at	ADP
fcis-22002	188	41	30db	30db	NOUN
fcis-22002	188	42	.	.	PUNCT
fcis-22002	189	1	this	this	PRON
fcis-22002	189	2	proves	prove	VERB
fcis-22002	189	3	the	the	DET
fcis-22002	189	4	robustness	robustness	NOUN
fcis-22002	189	5	of	of	ADP
fcis-22002	189	6	our	our	PRON
fcis-22002	189	7	method	method	NOUN
fcis-22002	189	8	to	to	PART
fcis-22002	189	9	noise	noise	NOUN
fcis-22002	189	10	interference	interference	NOUN
fcis-22002	189	11	.	.	PUNCT
fcis-22002	190	1	table	table	NOUN
fcis-22002	190	2	2	2	NUM
fcis-22002	190	3	.	.	PUNCT
fcis-22002	190	4	retrieval	retrieval	NOUN
fcis-22002	190	5	performance	performance	NOUN
fcis-22002	190	6	in	in	ADP
fcis-22002	190	7	different	different	ADJ
fcis-22002	190	8	noise	noise	NOUN
fcis-22002	190	9	environments	environment	NOUN
fcis-22002	190	10	retrieval	retrieval	NOUN
fcis-22002	190	11	environment	environment	NOUN
fcis-22002	190	12	retrieval	retrieval	NOUN
fcis-22002	190	13	of	of	ADP
fcis-22002	190	14	indicators	indicator	NOUN
fcis-22002	190	15	map	map	VERB
fcis-22002	190	16	mrr	mrr	PROPN
fcis-22002	190	17	p@1	p@1	PROPN
fcis-22002	190	18	p@5	p@5	PROPN
fcis-22002	190	19	p@9	p@9	PROPN
fcis-22002	190	20	clean	clean	VERB
fcis-22002	190	21	0.912	0.912	NUM
fcis-22002	190	22	0.984	0.984	NUM
fcis-22002	190	23	0.993	0.993	NUM
fcis-22002	190	24	0.981	0.981	NUM
fcis-22002	190	25	0.919	0.919	NUM
fcis-22002	190	26	snr=30db	snr=30db	NOUN
fcis-22002	190	27	0.817	0.817	NUM
fcis-22002	190	28	0.926	0.926	NUM
fcis-22002	190	29	0.966	0.966	NUM
fcis-22002	190	30	0.936	0.936	NUM
fcis-22002	190	31	0.833	0.833	NUM
fcis-22002	190	32	snr=25db	snr=25db	NOUN
fcis-22002	190	33	0.815	0.815	NUM
fcis-22002	190	34	0.939	0.939	NUM
fcis-22002	190	35	0.969	0.969	NUM
fcis-22002	190	36	0.937	0.937	NUM
fcis-22002	190	37	0.831	0.831	NUM
fcis-22002	190	38	snr=20db	snr=20db	NOUN
fcis-22002	190	39	0.755	0.755	NUM
fcis-22002	190	40	0.871	0.871	NUM
fcis-22002	190	41	0.957	0.957	NUM
fcis-22002	190	42	0.908	0.908	NUM
fcis-22002	190	43	0.775	0.775	NUM
fcis-22002	190	44	snr=15db	snr=15db	PROPN
fcis-22002	190	45	0.651	0.651	NUM
fcis-22002	190	46	0.791	0.791	NUM
fcis-22002	190	47	0.927	0.927	NUM
fcis-22002	190	48	0.845	0.845	NUM
fcis-22002	190	49	0.684	0.684	NUM
fcis-22002	190	50	snr=10db	snr=10db	NOUN
fcis-22002	190	51	0.473	0.473	NUM
fcis-22002	190	52	0.636	0.636	NUM
fcis-22002	190	53	0.860	0.860	NUM
fcis-22002	190	54	0.689	0.689	NUM
fcis-22002	190	55	0.526	0.526	NUM
fcis-22002	190	56	4.4	4.4	NUM
fcis-22002	190	57	.	.	PUNCT
fcis-22002	191	1	impact	impact	NOUN
fcis-22002	191	2	of	of	ADP
fcis-22002	191	3	pretraining	pretraine	VERB
fcis-22002	191	4	strategies	strategy	NOUN
fcis-22002	191	5	on	on	ADP
fcis-22002	191	6	speech	speech	NOUN
fcis-22002	191	7	retrieval	retrieval	NOUN
fcis-22002	191	8	in	in	ADP
fcis-22002	191	9	this	this	DET
fcis-22002	191	10	section	section	NOUN
fcis-22002	191	11	,	,	PUNCT
fcis-22002	191	12	the	the	DET
fcis-22002	191	13	effectiveness	effectiveness	NOUN
fcis-22002	191	14	of	of	ADP
fcis-22002	191	15	the	the	DET
fcis-22002	191	16	pre	pre	ADJ
fcis-22002	191	17	-	-	ADJ
fcis-22002	191	18	trained	train	VERB
fcis-22002	191	19	finetuned	finetune	VERB
fcis-22002	191	20	encoder	encoder	NOUN
fcis-22002	191	21	will	will	AUX
fcis-22002	191	22	be	be	AUX
fcis-22002	191	23	verified	verify	VERB
fcis-22002	191	24	by	by	ADP
fcis-22002	191	25	the	the	DET
fcis-22002	191	26	present	present	ADJ
fcis-22002	191	27	method	method	NOUN
fcis-22002	191	28	and	and	CCONJ
fcis-22002	191	29	the	the	DET
fcis-22002	191	30	following	follow	VERB
fcis-22002	191	31	two	two	NUM
fcis-22002	191	32	comparative	comparative	ADJ
fcis-22002	191	33	systems	system	NOUN
fcis-22002	191	34	.	.	PUNCT
fcis-22002	192	1	1.this	1.this	NUM
fcis-22002	192	2	method	method	NOUN
fcis-22002	192	3	:	:	PUNCT
fcis-22002	192	4	the	the	DET
fcis-22002	192	5	weight	weight	NOUN
fcis-22002	192	6	values	value	NOUN
fcis-22002	192	7	of	of	ADP
fcis-22002	192	8	the	the	DET
fcis-22002	192	9	encoder	encoder	NOUN
fcis-22002	192	10	module	module	NOUN
fcis-22002	192	11	are	be	AUX
fcis-22002	192	12	obtained	obtain	VERB
fcis-22002	192	13	by	by	ADP
fcis-22002	192	14	pre	pre	ADJ
fcis-22002	192	15	-	-	NOUN
fcis-22002	192	16	training	training	NOUN
fcis-22002	192	17	,	,	PUNCT
fcis-22002	192	18	and	and	CCONJ
fcis-22002	192	19	the	the	DET
fcis-22002	192	20	weight	weight	NOUN
fcis-22002	192	21	values	value	NOUN
fcis-22002	192	22	will	will	AUX
fcis-22002	192	23	not	not	PART
fcis-22002	192	24	be	be	AUX
fcis-22002	192	25	frozen	freeze	VERB
fcis-22002	192	26	during	during	ADP
fcis-22002	192	27	the	the	DET
fcis-22002	192	28	classification	classification	NOUN
fcis-22002	192	29	training	training	NOUN
fcis-22002	192	30	process	process	NOUN
fcis-22002	192	31	.	.	PUNCT
fcis-22002	193	1	2.pre	2.pre	NOUN
fcis-22002	193	2	-	-	NOUN
fcis-22002	193	3	training	training	NOUN
fcis-22002	193	4	and	and	CCONJ
fcis-22002	193	5	frozen	frozen	ADJ
fcis-22002	193	6	encoder	encoder	NOUN
fcis-22002	193	7	weights	weight	VERB
fcis-22002	193	8	:	:	PUNCT
fcis-22002	193	9	the	the	DET
fcis-22002	193	10	weight	weight	NOUN
fcis-22002	193	11	values	value	NOUN
fcis-22002	193	12	of	of	ADP
fcis-22002	193	13	the	the	DET
fcis-22002	193	14	encoder	encoder	NOUN
fcis-22002	193	15	module	module	NOUN
fcis-22002	193	16	are	be	AUX
fcis-22002	193	17	obtained	obtain	VERB
fcis-22002	193	18	by	by	ADP
fcis-22002	193	19	pre	pre	ADJ
fcis-22002	193	20	-	-	NOUN
fcis-22002	193	21	training	training	NOUN
fcis-22002	193	22	,	,	PUNCT
fcis-22002	193	23	and	and	CCONJ
fcis-22002	193	24	the	the	DET
fcis-22002	193	25	weight	weight	NOUN
fcis-22002	193	26	values	value	NOUN
fcis-22002	193	27	are	be	AUX
fcis-22002	193	28	frozen	freeze	VERB
fcis-22002	193	29	during	during	ADP
fcis-22002	193	30	the	the	DET
fcis-22002	193	31	classification	classification	NOUN
fcis-22002	193	32	training	training	NOUN
fcis-22002	193	33	.	.	PUNCT
fcis-22002	194	1	3	3	X
fcis-22002	194	2	.	.	X
fcis-22002	194	3	no	no	DET
fcis-22002	194	4	pre	pre	NOUN
fcis-22002	194	5	-	-	NOUN
fcis-22002	194	6	training	training	NOUN
fcis-22002	194	7	:	:	PUNCT
fcis-22002	194	8	direct	direct	ADJ
fcis-22002	194	9	classification	classification	NOUN
fcis-22002	194	10	training	training	NOUN
fcis-22002	194	11	without	without	ADP
fcis-22002	194	12	27	27	NUM
fcis-22002	194	13	pre	pre	ADJ
fcis-22002	194	14	-	-	ADJ
fcis-22002	194	15	training	training	ADJ
fcis-22002	194	16	table	table	NOUN
fcis-22002	194	17	3	3	NUM
fcis-22002	194	18	.	.	PUNCT
fcis-22002	194	19	impact	impact	NOUN
fcis-22002	194	20	of	of	ADP
fcis-22002	194	21	pretraining	pretraine	VERB
fcis-22002	194	22	strategies	strategy	NOUN
fcis-22002	194	23	on	on	ADP
fcis-22002	194	24	retrieval	retrieval	NOUN
fcis-22002	194	25	performance	performance	NOUN
fcis-22002	194	26	pre	pre	ADJ
fcis-22002	194	27	-	-	ADJ
fcis-22002	194	28	training	training	ADJ
fcis-22002	194	29	strategies	strategy	NOUN
fcis-22002	194	30	retrieval	retrieval	NOUN
fcis-22002	194	31	of	of	ADP
fcis-22002	194	32	indicators	indicator	NOUN
fcis-22002	194	33	map	map	VERB
fcis-22002	194	34	mrr	mrr	PROPN
fcis-22002	194	35	p@1	p@1	PROPN
fcis-22002	194	36	p@5	p@5	PROPN
fcis-22002	194	37	p@9	p@9	PROPN
fcis-22002	194	38	pre	pre	ADJ
fcis-22002	194	39	-	-	VERB
fcis-22002	194	40	training	training	ADJ
fcis-22002	194	41	and	and	CCONJ
fcis-22002	194	42	fine	fine	ADV
fcis-22002	194	43	-	-	PUNCT
fcis-22002	194	44	tuning	tune	VERB
fcis-22002	194	45	0.912	0.912	NUM
fcis-22002	194	46	0.984	0.984	NUM
fcis-22002	194	47	0.993	0.993	NUM
fcis-22002	194	48	0.981	0.981	NUM
fcis-22002	194	49	0.919	0.919	NUM
fcis-22002	194	50	pre	pre	VERB
fcis-22002	194	51	-	-	VERB
fcis-22002	194	52	trained	train	VERB
fcis-22002	194	53	with	with	ADP
fcis-22002	194	54	frozen	frozen	ADJ
fcis-22002	194	55	weights	weight	NOUN
fcis-22002	194	56	0.802	0.802	NUM
fcis-22002	194	57	0.895	0.895	NUM
fcis-22002	194	58	0.970	0.970	NUM
fcis-22002	194	59	0.932	0.932	NUM
fcis-22002	194	60	0.821	0.821	NUM
fcis-22002	194	61	no	no	DET
fcis-22002	194	62	pre	pre	NOUN
fcis-22002	194	63	-	-	NOUN
fcis-22002	194	64	training	train	VERB
fcis-22002	194	65	0.786	0.786	NUM
fcis-22002	194	66	0.903	0.903	NUM
fcis-22002	194	67	0.956	0.956	NUM
fcis-22002	194	68	0.918	0.918	NUM
fcis-22002	194	69	0.806	0.806	NUM
fcis-22002	194	70	the	the	DET
fcis-22002	194	71	experimental	experimental	ADJ
fcis-22002	194	72	results	result	NOUN
fcis-22002	194	73	are	be	AUX
fcis-22002	194	74	shown	show	VERB
fcis-22002	194	75	in	in	ADP
fcis-22002	194	76	table	table	NOUN
fcis-22002	194	77	3	3	NUM
fcis-22002	194	78	,	,	PUNCT
fcis-22002	194	79	where	where	SCONJ
fcis-22002	194	80	pretraining	pretraine	VERB
fcis-22002	194	81	the	the	DET
fcis-22002	194	82	encoder	encoder	NOUN
fcis-22002	194	83	can	can	AUX
fcis-22002	194	84	enhance	enhance	VERB
fcis-22002	194	85	significantly	significantly	ADV
fcis-22002	194	86	improve	improve	VERB
fcis-22002	194	87	the	the	DET
fcis-22002	194	88	performance	performance	NOUN
fcis-22002	194	89	of	of	ADP
fcis-22002	194	90	the	the	DET
fcis-22002	194	91	model	model	NOUN
fcis-22002	194	92	.	.	PUNCT
fcis-22002	195	1	in	in	ADP
fcis-22002	195	2	addition	addition	NOUN
fcis-22002	195	3	,	,	PUNCT
fcis-22002	195	4	using	use	VERB
fcis-22002	195	5	a	a	DET
fcis-22002	195	6	fine	fine	ADJ
fcis-22002	195	7	-	-	PUNCT
fcis-22002	195	8	tuning	tune	VERB
fcis-22002	195	9	strategy	strategy	NOUN
fcis-22002	195	10	can	can	AUX
fcis-22002	195	11	yield	yield	VERB
fcis-22002	195	12	better	well	ADJ
fcis-22002	195	13	results	result	NOUN
fcis-22002	195	14	than	than	ADP
fcis-22002	195	15	freezing	freeze	VERB
fcis-22002	195	16	the	the	DET
fcis-22002	195	17	layer	layer	NOUN
fcis-22002	195	18	weights	weight	NOUN
fcis-22002	195	19	.	.	PUNCT
fcis-22002	196	1	pre	pre	VERB
fcis-22002	196	2	-	-	NOUN
fcis-22002	196	3	training	training	NOUN
fcis-22002	196	4	can	can	AUX
fcis-22002	196	5	help	help	VERB
fcis-22002	196	6	the	the	DET
fcis-22002	196	7	model	model	NOUN
fcis-22002	196	8	to	to	PART
fcis-22002	196	9	learn	learn	VERB
fcis-22002	196	10	features	feature	NOUN
fcis-22002	196	11	that	that	PRON
fcis-22002	196	12	are	be	AUX
fcis-22002	196	13	relevant	relevant	ADJ
fcis-22002	196	14	to	to	ADP
fcis-22002	196	15	the	the	DET
fcis-22002	196	16	speech	speech	NOUN
fcis-22002	196	17	content	content	NOUN
fcis-22002	196	18	and	and	CCONJ
fcis-22002	196	19	the	the	DET
fcis-22002	196	20	simple	simple	ADJ
fcis-22002	196	21	retrieval	retrieval	NOUN
fcis-22002	196	22	training	training	NOUN
fcis-22002	196	23	may	may	AUX
fcis-22002	196	24	not	not	PART
fcis-22002	196	25	be	be	AUX
fcis-22002	196	26	effective	effective	ADJ
fcis-22002	196	27	in	in	ADP
fcis-22002	196	28	updating	update	VERB
fcis-22002	196	29	the	the	DET
fcis-22002	196	30	deeper	deep	ADJ
fcis-22002	196	31	parameters	parameter	NOUN
fcis-22002	196	32	in	in	ADP
fcis-22002	196	33	the	the	DET
fcis-22002	196	34	network	network	NOUN
fcis-22002	196	35	,	,	PUNCT
fcis-22002	196	36	which	which	PRON
fcis-22002	196	37	can	can	AUX
fcis-22002	196	38	be	be	AUX
fcis-22002	196	39	alleviated	alleviate	VERB
fcis-22002	196	40	by	by	ADP
fcis-22002	196	41	pretraining	pretraine	VERB
fcis-22002	196	42	.	.	PUNCT
fcis-22002	197	1	a	a	DET
fcis-22002	197	2	fine	fine	ADV
fcis-22002	197	3	-	-	PUNCT
fcis-22002	197	4	tuning	tune	VERB
fcis-22002	197	5	strategy	strategy	NOUN
fcis-22002	197	6	allows	allow	VERB
fcis-22002	197	7	the	the	DET
fcis-22002	197	8	model	model	NOUN
fcis-22002	197	9	to	to	PART
fcis-22002	197	10	be	be	AUX
fcis-22002	197	11	personalized	personalize	VERB
fcis-22002	197	12	for	for	ADP
fcis-22002	197	13	a	a	DET
fcis-22002	197	14	specific	specific	ADJ
fcis-22002	197	15	task	task	NOUN
fcis-22002	197	16	while	while	SCONJ
fcis-22002	197	17	maintaining	maintain	VERB
fcis-22002	197	18	the	the	DET
fcis-22002	197	19	knowledge	knowledge	NOUN
fcis-22002	197	20	learned	learn	VERB
fcis-22002	197	21	by	by	ADP
fcis-22002	197	22	the	the	DET
fcis-22002	197	23	pretrained	pretraine	VERB
fcis-22002	197	24	model	model	NOUN
fcis-22002	197	25	,	,	PUNCT
fcis-22002	197	26	allowing	allow	VERB
fcis-22002	197	27	the	the	DET
fcis-22002	197	28	model	model	NOUN
fcis-22002	197	29	to	to	PART
fcis-22002	197	30	adjust	adjust	VERB
fcis-22002	197	31	its	its	PRON
fcis-22002	197	32	parameters	parameter	NOUN
fcis-22002	197	33	more	more	ADV
fcis-22002	197	34	finely	finely	ADV
fcis-22002	197	35	to	to	PART
fcis-22002	197	36	optimize	optimize	VERB
fcis-22002	197	37	performance	performance	NOUN
fcis-22002	197	38	for	for	ADP
fcis-22002	197	39	a	a	DET
fcis-22002	197	40	specific	specific	ADJ
fcis-22002	197	41	task	task	NOUN
fcis-22002	197	42	.	.	PUNCT
fcis-22002	198	1	4.5	4.5	NUM
fcis-22002	198	2	.	.	PUNCT
fcis-22002	198	3	impact	impact	NOUN
fcis-22002	198	4	of	of	ADP
fcis-22002	198	5	pre	pre	ADJ
fcis-22002	198	6	-	-	ADJ
fcis-22002	198	7	trained	train	VERB
fcis-22002	198	8	labels	label	NOUN
fcis-22002	198	9	on	on	ADP
fcis-22002	198	10	speech	speech	NOUN
fcis-22002	198	11	retrieval	retrieval	NOUN
fcis-22002	198	12	this	this	DET
fcis-22002	198	13	section	section	NOUN
fcis-22002	198	14	analyzes	analyze	VERB
fcis-22002	198	15	the	the	DET
fcis-22002	198	16	effect	effect	NOUN
fcis-22002	198	17	of	of	ADP
fcis-22002	198	18	pre	pre	ADJ
fcis-22002	198	19	-	-	ADJ
fcis-22002	198	20	trained	train	VERB
fcis-22002	198	21	labels	label	NOUN
fcis-22002	198	22	on	on	ADP
fcis-22002	198	23	speech	speech	NOUN
fcis-22002	198	24	retrieval	retrieval	NOUN
fcis-22002	198	25	by	by	ADP
fcis-22002	198	26	using	use	VERB
fcis-22002	198	27	chinese	chinese	ADJ
fcis-22002	198	28	characters	character	NOUN
fcis-22002	198	29	as	as	ADP
fcis-22002	198	30	pre	pre	ADJ
fcis-22002	198	31	-	-	ADJ
fcis-22002	198	32	trained	train	VERB
fcis-22002	198	33	labels	label	NOUN
fcis-22002	198	34	in	in	ADP
fcis-22002	198	35	comparison	comparison	NOUN
fcis-22002	198	36	to	to	ADP
fcis-22002	198	37	phonemes	phoneme	NOUN
fcis-22002	198	38	as	as	ADP
fcis-22002	198	39	labels	label	NOUN
fcis-22002	198	40	for	for	ADP
fcis-22002	198	41	pre	pre	ADJ
fcis-22002	198	42	-	-	ADJ
fcis-22002	198	43	training	training	ADJ
fcis-22002	198	44	.	.	PUNCT
fcis-22002	199	1	table	table	NOUN
fcis-22002	199	2	4	4	NUM
fcis-22002	199	3	.	.	PUNCT
fcis-22002	199	4	impact	impact	NOUN
fcis-22002	199	5	of	of	ADP
fcis-22002	199	6	pre	pre	ADJ
fcis-22002	199	7	-	-	ADJ
fcis-22002	199	8	trained	train	VERB
fcis-22002	199	9	labels	label	NOUN
fcis-22002	199	10	on	on	ADP
fcis-22002	199	11	retrieval	retrieval	NOUN
fcis-22002	199	12	performance	performance	NOUN
fcis-22002	199	13	pre	pre	ADJ
fcis-22002	199	14	-	-	ADJ
fcis-22002	199	15	training	training	ADJ
fcis-22002	199	16	labels	label	NOUN
fcis-22002	199	17	retrieval	retrieval	NOUN
fcis-22002	199	18	of	of	ADP
fcis-22002	199	19	indicators	indicator	NOUN
fcis-22002	199	20	map	map	VERB
fcis-22002	199	21	mrr	mrr	PROPN
fcis-22002	199	22	p@1	p@1	PROPN
fcis-22002	199	23	p@5	p@5	PROPN
fcis-22002	199	24	p@9	p@9	PROPN
fcis-22002	199	25	phoneme	phoneme	VERB
fcis-22002	199	26	0.912	0.912	NUM
fcis-22002	199	27	0.984	0.984	NUM
fcis-22002	199	28	0.993	0.993	NUM
fcis-22002	199	29	0.981	0.981	NUM
fcis-22002	199	30	0.919	0.919	NUM
fcis-22002	199	31	chinese	chinese	ADJ
fcis-22002	199	32	character	character	NOUN
fcis-22002	199	33	0.846	0.846	NOUN
fcis-22002	199	34	0.959	0.959	NUM
fcis-22002	199	35	0.979	0.979	NUM
fcis-22002	199	36	0.955	0.955	NUM
fcis-22002	199	37	0.859	0.859	NUM
fcis-22002	199	38	the	the	DET
fcis-22002	199	39	experimental	experimental	ADJ
fcis-22002	199	40	results	result	NOUN
fcis-22002	199	41	are	be	AUX
fcis-22002	199	42	displayed	display	VERB
fcis-22002	199	43	in	in	ADP
fcis-22002	199	44	table	table	NOUN
fcis-22002	199	45	4	4	NUM
fcis-22002	199	46	.	.	PUNCT
fcis-22002	200	1	the	the	DET
fcis-22002	200	2	above	above	ADJ
fcis-22002	200	3	experimental	experimental	ADJ
fcis-22002	200	4	results	result	NOUN
fcis-22002	200	5	show	show	VERB
fcis-22002	200	6	that	that	SCONJ
fcis-22002	200	7	the	the	DET
fcis-22002	200	8	model	model	NOUN
fcis-22002	200	9	is	be	AUX
fcis-22002	200	10	able	able	ADJ
fcis-22002	200	11	to	to	PART
fcis-22002	200	12	achieve	achieve	VERB
fcis-22002	200	13	better	well	ADJ
fcis-22002	200	14	performance	performance	NOUN
fcis-22002	200	15	using	use	VERB
fcis-22002	200	16	phonemes	phoneme	NOUN
fcis-22002	200	17	as	as	ADP
fcis-22002	200	18	pre	pre	ADJ
fcis-22002	200	19	-	-	ADJ
fcis-22002	200	20	trained	train	VERB
fcis-22002	200	21	labels	label	NOUN
fcis-22002	200	22	compared	compare	VERB
fcis-22002	200	23	to	to	ADP
fcis-22002	200	24	using	use	VERB
fcis-22002	200	25	chinese	chinese	ADJ
fcis-22002	200	26	characters	character	NOUN
fcis-22002	200	27	as	as	ADP
fcis-22002	200	28	pre	pre	ADJ
fcis-22002	200	29	-	-	ADJ
fcis-22002	200	30	trained	train	VERB
fcis-22002	200	31	labels	label	NOUN
fcis-22002	200	32	for	for	ADP
fcis-22002	200	33	speech	speech	NOUN
fcis-22002	200	34	recognition	recognition	NOUN
fcis-22002	200	35	.	.	PUNCT
fcis-22002	201	1	the	the	DET
fcis-22002	201	2	reason	reason	NOUN
fcis-22002	201	3	for	for	ADP
fcis-22002	201	4	this	this	PRON
fcis-22002	201	5	is	be	AUX
fcis-22002	201	6	that	that	SCONJ
fcis-22002	201	7	the	the	DET
fcis-22002	201	8	features	feature	NOUN
fcis-22002	201	9	learned	learn	VERB
fcis-22002	201	10	by	by	ADP
fcis-22002	201	11	the	the	DET
fcis-22002	201	12	model	model	NOUN
fcis-22002	201	13	using	use	VERB
fcis-22002	201	14	phonemes	phoneme	NOUN
fcis-22002	201	15	as	as	SCONJ
fcis-22002	201	16	pre	pre	ADJ
fcis-22002	201	17	-	-	ADJ
fcis-22002	201	18	trained	train	VERB
fcis-22002	201	19	labels	label	NOUN
fcis-22002	201	20	for	for	ADP
fcis-22002	201	21	speech	speech	NOUN
fcis-22002	201	22	recognition	recognition	NOUN
fcis-22002	201	23	are	be	AUX
fcis-22002	201	24	more	more	ADV
fcis-22002	201	25	specific	specific	ADJ
fcis-22002	201	26	,	,	PUNCT
fcis-22002	201	27	better	well	ADV
fcis-22002	201	28	generalized	generalize	VERB
fcis-22002	201	29	and	and	CCONJ
fcis-22002	201	30	more	more	ADV
fcis-22002	201	31	applicable	applicable	ADJ
fcis-22002	201	32	than	than	ADP
fcis-22002	201	33	those	those	PRON
fcis-22002	201	34	learned	learn	VERB
fcis-22002	201	35	using	use	VERB
fcis-22002	201	36	words	word	NOUN
fcis-22002	201	37	as	as	ADP
fcis-22002	201	38	labels	label	NOUN
fcis-22002	201	39	.	.	PUNCT
fcis-22002	202	1	therefore	therefore	ADV
fcis-22002	202	2	,	,	PUNCT
fcis-22002	202	3	when	when	SCONJ
fcis-22002	202	4	tested	test	VERB
fcis-22002	202	5	on	on	ADP
fcis-22002	202	6	the	the	DET
fcis-22002	202	7	test	test	NOUN
fcis-22002	202	8	set	set	NOUN
fcis-22002	202	9	,	,	PUNCT
fcis-22002	202	10	its	its	PRON
fcis-22002	202	11	performance	performance	NOUN
fcis-22002	202	12	is	be	AUX
fcis-22002	202	13	better	well	ADJ
fcis-22002	202	14	than	than	ADP
fcis-22002	202	15	that	that	PRON
fcis-22002	202	16	of	of	ADP
fcis-22002	202	17	using	use	VERB
fcis-22002	202	18	linguistic	linguistic	ADJ
fcis-22002	202	19	text	text	NOUN
fcis-22002	202	20	as	as	ADP
fcis-22002	202	21	labels	label	NOUN
fcis-22002	202	22	.	.	PUNCT
fcis-22002	203	1	4.6	4.6	NUM
fcis-22002	203	2	.	.	PUNCT
fcis-22002	204	1	the	the	DET
fcis-22002	204	2	effect	effect	NOUN
fcis-22002	204	3	of	of	ADP
fcis-22002	204	4	downsampling	downsample	VERB
fcis-22002	204	5	modules	module	NOUN
fcis-22002	204	6	on	on	ADP
fcis-22002	204	7	speech	speech	NOUN
fcis-22002	204	8	retrieval	retrieval	NOUN
fcis-22002	204	9	this	this	DET
fcis-22002	204	10	section	section	NOUN
fcis-22002	204	11	verifies	verify	VERB
fcis-22002	204	12	the	the	DET
fcis-22002	204	13	effectiveness	effectiveness	NOUN
fcis-22002	204	14	of	of	ADP
fcis-22002	204	15	the	the	DET
fcis-22002	204	16	downsampling	downsample	VERB
fcis-22002	204	17	module	module	NOUN
fcis-22002	204	18	by	by	ADP
fcis-22002	204	19	comparing	compare	VERB
fcis-22002	204	20	two	two	NUM
fcis-22002	204	21	systems	system	NOUN
fcis-22002	204	22	.	.	PUNCT
fcis-22002	205	1	1	1	X
fcis-22002	205	2	.	.	X
fcis-22002	205	3	no	no	DET
fcis-22002	205	4	downsampling	downsample	VERB
fcis-22002	205	5	module	module	NOUN
fcis-22002	205	6	:	:	PUNCT
fcis-22002	205	7	the	the	DET
fcis-22002	205	8	features	feature	NOUN
fcis-22002	205	9	obtained	obtain	VERB
fcis-22002	205	10	from	from	ADP
fcis-22002	205	11	the	the	DET
fcis-22002	205	12	encoder	encoder	NOUN
fcis-22002	205	13	go	go	VERB
fcis-22002	205	14	directly	directly	ADV
fcis-22002	205	15	to	to	ADP
fcis-22002	205	16	the	the	DET
fcis-22002	205	17	decoder	decoder	NOUN
fcis-22002	205	18	without	without	ADP
fcis-22002	205	19	any	any	DET
fcis-22002	205	20	change	change	NOUN
fcis-22002	205	21	in	in	ADP
fcis-22002	205	22	its	its	PRON
fcis-22002	205	23	time	time	NOUN
fcis-22002	205	24	step	step	NOUN
fcis-22002	205	25	.	.	PUNCT
fcis-22002	206	1	2	2	X
fcis-22002	206	2	.	.	X
fcis-22002	206	3	convolutional	convolutional	ADJ
fcis-22002	206	4	pooling	pooling	NOUN
fcis-22002	206	5	downsampling	downsampling	NOUN
fcis-22002	206	6	:	:	PUNCT
fcis-22002	206	7	features	feature	NOUN
fcis-22002	206	8	obtained	obtain	VERB
fcis-22002	206	9	from	from	ADP
fcis-22002	206	10	the	the	DET
fcis-22002	206	11	encoder	encoder	NOUN
fcis-22002	206	12	are	be	AUX
fcis-22002	206	13	downsampled	downsample	VERB
fcis-22002	206	14	by	by	ADP
fcis-22002	206	15	one	one	NUM
fcis-22002	206	16	-	-	PUNCT
fcis-22002	206	17	dimensional	dimensional	ADJ
fcis-22002	206	18	convolution	convolution	NOUN
fcis-22002	206	19	and	and	CCONJ
fcis-22002	206	20	maximum	maximum	ADJ
fcis-22002	206	21	pooling	pooling	NOUN
fcis-22002	206	22	before	before	ADP
fcis-22002	206	23	entering	enter	VERB
fcis-22002	206	24	the	the	DET
fcis-22002	206	25	decoder	decoder	NOUN
fcis-22002	206	26	.	.	PUNCT
fcis-22002	207	1	3	3	X
fcis-22002	207	2	.	.	X
fcis-22002	207	3	causal	causal	ADJ
fcis-22002	207	4	convolutional	convolutional	ADJ
fcis-22002	207	5	pooling	pooling	NOUN
fcis-22002	207	6	downsampling	downsampling	NOUN
fcis-22002	207	7	:	:	PUNCT
fcis-22002	207	8	features	feature	NOUN
fcis-22002	207	9	obtained	obtain	VERB
fcis-22002	207	10	from	from	ADP
fcis-22002	207	11	the	the	DET
fcis-22002	207	12	encoder	encoder	NOUN
fcis-22002	207	13	are	be	AUX
fcis-22002	207	14	downsampled	downsample	VERB
fcis-22002	207	15	by	by	ADP
fcis-22002	207	16	causal	causal	ADJ
fcis-22002	207	17	convolution	convolution	NOUN
fcis-22002	207	18	and	and	CCONJ
fcis-22002	207	19	maximum	maximum	ADJ
fcis-22002	207	20	pooling	pooling	NOUN
fcis-22002	207	21	before	before	ADP
fcis-22002	207	22	entering	enter	VERB
fcis-22002	207	23	the	the	DET
fcis-22002	207	24	decoder	decoder	NOUN
fcis-22002	207	25	.	.	PUNCT
fcis-22002	208	1	table	table	NOUN
fcis-22002	208	2	5	5	NUM
fcis-22002	208	3	.	.	PUNCT
fcis-22002	209	1	impact	impact	NOUN
fcis-22002	209	2	of	of	ADP
fcis-22002	209	3	the	the	DET
fcis-22002	209	4	downsampling	downsample	VERB
fcis-22002	209	5	module	module	NOUN
fcis-22002	209	6	on	on	ADP
fcis-22002	209	7	retrieval	retrieval	NOUN
fcis-22002	209	8	performance	performance	NOUN
fcis-22002	209	9	downsampling	downsample	VERB
fcis-22002	209	10	module	module	NOUN
fcis-22002	209	11	retrieval	retrieval	NOUN
fcis-22002	209	12	of	of	ADP
fcis-22002	209	13	indicators	indicator	NOUN
fcis-22002	209	14	map	map	VERB
fcis-22002	209	15	mrr	mrr	PROPN
fcis-22002	209	16	p@1	p@1	PROPN
fcis-22002	209	17	p@5	p@5	PROPN
fcis-22002	209	18	p@9	p@9	PROPN
fcis-22002	209	19	not	not	PART
fcis-22002	209	20	have	have	VERB
fcis-22002	209	21	0.683	0.683	NUM
fcis-22002	209	22	0.841	0.841	NUM
fcis-22002	209	23	0.933	0.933	NUM
fcis-22002	209	24	0.854	0.854	NUM
fcis-22002	209	25	0.718	0.718	NUM
fcis-22002	209	26	convolution	convolution	NOUN
fcis-22002	209	27	pooling	pool	VERB
fcis-22002	209	28	0.894	0.894	NUM
fcis-22002	209	29	0.969	0.969	NUM
fcis-22002	209	30	0.981	0.981	NUM
fcis-22002	209	31	0.953	0.953	NUM
fcis-22002	209	32	0.905	0.905	NUM
fcis-22002	209	33	causal	causal	ADJ
fcis-22002	209	34	convolution	convolution	NOUN
fcis-22002	209	35	pooling	pool	VERB
fcis-22002	209	36	0.912	0.912	NUM
fcis-22002	209	37	0.984	0.984	NUM
fcis-22002	209	38	0.993	0.993	NUM
fcis-22002	209	39	0.981	0.981	NUM
fcis-22002	209	40	0.919	0.919	NUM
fcis-22002	209	41	the	the	DET
fcis-22002	209	42	experimental	experimental	ADJ
fcis-22002	209	43	results	result	NOUN
fcis-22002	209	44	are	be	AUX
fcis-22002	209	45	shown	show	VERB
fcis-22002	209	46	in	in	ADP
fcis-22002	209	47	table	table	NOUN
fcis-22002	209	48	5	5	NUM
fcis-22002	209	49	,	,	PUNCT
fcis-22002	209	50	compared	compare	VERB
fcis-22002	209	51	with	with	ADP
fcis-22002	209	52	the	the	DET
fcis-22002	209	53	model	model	NOUN
fcis-22002	209	54	without	without	ADP
fcis-22002	209	55	downsampling	downsample	VERB
fcis-22002	209	56	module	module	NOUN
fcis-22002	209	57	,	,	PUNCT
fcis-22002	209	58	the	the	DET
fcis-22002	209	59	use	use	NOUN
fcis-22002	209	60	of	of	ADP
fcis-22002	209	61	downsampling	downsample	VERB
fcis-22002	209	62	module	module	NOUN
fcis-22002	209	63	significantly	significantly	ADV
fcis-22002	209	64	improves	improve	VERB
fcis-22002	209	65	the	the	DET
fcis-22002	209	66	model	model	NOUN
fcis-22002	209	67	performance	performance	NOUN
fcis-22002	209	68	,	,	PUNCT
fcis-22002	209	69	and	and	CCONJ
fcis-22002	209	70	the	the	DET
fcis-22002	209	71	use	use	NOUN
fcis-22002	209	72	of	of	ADP
fcis-22002	209	73	causal	causal	ADJ
fcis-22002	209	74	convolution	convolution	NOUN
fcis-22002	209	75	can	can	AUX
fcis-22002	209	76	obtain	obtain	VERB
fcis-22002	209	77	better	well	ADJ
fcis-22002	209	78	performance	performance	NOUN
fcis-22002	209	79	than	than	ADP
fcis-22002	209	80	ordinary	ordinary	ADJ
fcis-22002	209	81	convolution	convolution	NOUN
fcis-22002	209	82	.	.	PUNCT
fcis-22002	210	1	ctc	ctc	NOUN
fcis-22002	210	2	recognition	recognition	PROPN
fcis-22002	210	3	model	model	NOUN
fcis-22002	210	4	in	in	ADP
fcis-22002	210	5	the	the	DET
fcis-22002	210	6	recognition	recognition	NOUN
fcis-22002	210	7	task	task	NOUN
fcis-22002	210	8	will	will	AUX
fcis-22002	210	9	be	be	AUX
fcis-22002	210	10	recognized	recognize	VERB
fcis-22002	210	11	and	and	CCONJ
fcis-22002	210	12	classified	classify	VERB
fcis-22002	210	13	at	at	ADP
fcis-22002	210	14	each	each	DET
fcis-22002	210	15	time	time	NOUN
fcis-22002	210	16	step	step	NOUN
fcis-22002	210	17	,	,	PUNCT
fcis-22002	210	18	and	and	CCONJ
fcis-22002	210	19	its	its	PRON
fcis-22002	210	20	classification	classification	NOUN
fcis-22002	210	21	results	result	NOUN
fcis-22002	210	22	will	will	AUX
fcis-22002	210	23	be	be	AUX
fcis-22002	210	24	repeated	repeat	VERB
fcis-22002	210	25	in	in	ADP
fcis-22002	210	26	a	a	DET
fcis-22002	210	27	large	large	ADJ
fcis-22002	210	28	number	number	NOUN
fcis-22002	210	29	of	of	ADP
fcis-22002	210	30	neighboring	neighboring	NOUN
fcis-22002	210	31	time	time	NOUN
fcis-22002	210	32	steps	step	NOUN
fcis-22002	210	33	,	,	PUNCT
fcis-22002	210	34	and	and	CCONJ
fcis-22002	210	35	the	the	DET
fcis-22002	210	36	features	feature	NOUN
fcis-22002	210	37	obtained	obtain	VERB
fcis-22002	210	38	by	by	ADP
fcis-22002	210	39	the	the	DET
fcis-22002	210	40	pre	pre	ADJ
fcis-22002	210	41	-	-	ADJ
fcis-22002	210	42	training	training	NOUN
fcis-22002	210	43	module	module	NOUN
fcis-22002	210	44	will	will	AUX
fcis-22002	210	45	be	be	AUX
fcis-22002	210	46	in	in	ADP
fcis-22002	210	47	such	such	DET
fcis-22002	210	48	a	a	DET
fcis-22002	210	49	situation	situation	NOUN
fcis-22002	210	50	,	,	PUNCT
fcis-22002	210	51	too	too	ADV
fcis-22002	210	52	long	long	ADV
fcis-22002	210	53	and	and	CCONJ
fcis-22002	210	54	containing	contain	VERB
fcis-22002	210	55	redundant	redundant	ADJ
fcis-22002	210	56	information	information	NOUN
fcis-22002	210	57	will	will	AUX
fcis-22002	210	58	affect	affect	VERB
fcis-22002	210	59	the	the	DET
fcis-22002	210	60	training	training	NOUN
fcis-22002	210	61	of	of	ADP
fcis-22002	210	62	subsequent	subsequent	ADJ
fcis-22002	210	63	modules	module	NOUN
fcis-22002	210	64	.	.	PUNCT
fcis-22002	211	1	the	the	DET
fcis-22002	211	2	features	feature	NOUN
fcis-22002	211	3	obtained	obtain	VERB
fcis-22002	211	4	through	through	ADP
fcis-22002	211	5	its	its	PRON
fcis-22002	211	6	pre	pre	ADJ
fcis-22002	211	7	-	-	ADJ
fcis-22002	211	8	training	training	NOUN
fcis-22002	211	9	module	module	NOUN
fcis-22002	211	10	will	will	AUX
fcis-22002	211	11	also	also	ADV
fcis-22002	211	12	be	be	AUX
fcis-22002	211	13	in	in	ADP
fcis-22002	211	14	this	this	DET
fcis-22002	211	15	situation	situation	NOUN
fcis-22002	211	16	,	,	PUNCT
fcis-22002	211	17	and	and	CCONJ
fcis-22002	211	18	features	feature	NOUN
fcis-22002	211	19	that	that	PRON
fcis-22002	211	20	are	be	AUX
fcis-22002	211	21	too	too	ADV
fcis-22002	211	22	long	long	ADJ
fcis-22002	211	23	and	and	CCONJ
fcis-22002	211	24	contain	contain	VERB
fcis-22002	211	25	redundant	redundant	ADJ
fcis-22002	211	26	information	information	NOUN
fcis-22002	211	27	will	will	AUX
fcis-22002	211	28	affect	affect	VERB
fcis-22002	211	29	the	the	DET
fcis-22002	211	30	training	training	NOUN
fcis-22002	211	31	of	of	ADP
fcis-22002	211	32	the	the	DET
fcis-22002	211	33	subsequent	subsequent	ADJ
fcis-22002	211	34	modules	module	NOUN
fcis-22002	211	35	,	,	PUNCT
fcis-22002	211	36	so	so	SCONJ
fcis-22002	211	37	sequence	sequence	NOUN
fcis-22002	211	38	integration	integration	NOUN
fcis-22002	211	39	and	and	CCONJ
fcis-22002	211	40	feature	feature	NOUN
fcis-22002	211	41	selection	selection	NOUN
fcis-22002	211	42	through	through	ADP
fcis-22002	211	43	the	the	DET
fcis-22002	211	44	downsampling	downsample	VERB
fcis-22002	211	45	module	module	NOUN
fcis-22002	211	46	can	can	AUX
fcis-22002	211	47	improve	improve	VERB
fcis-22002	211	48	the	the	DET
fcis-22002	211	49	performance	performance	NOUN
fcis-22002	211	50	of	of	ADP
fcis-22002	211	51	the	the	DET
fcis-22002	211	52	model	model	NOUN
fcis-22002	211	53	.	.	PUNCT
fcis-22002	212	1	4.7	4.7	NUM
fcis-22002	212	2	.	.	PUNCT
fcis-22002	212	3	effect	effect	NOUN
fcis-22002	212	4	of	of	ADP
fcis-22002	212	5	different	different	ADJ
fcis-22002	212	6	hash	hash	NOUN
fcis-22002	212	7	code	code	NOUN
fcis-22002	212	8	lengths	length	NOUN
fcis-22002	212	9	on	on	ADP
fcis-22002	212	10	retrieval	retrieval	NOUN
fcis-22002	212	11	performance	performance	NOUN
fcis-22002	212	12	this	this	DET
fcis-22002	212	13	section	section	NOUN
fcis-22002	212	14	tests	test	VERB
fcis-22002	212	15	the	the	DET
fcis-22002	212	16	effect	effect	NOUN
fcis-22002	212	17	of	of	ADP
fcis-22002	212	18	using	use	VERB
fcis-22002	212	19	different	different	ADJ
fcis-22002	212	20	hash	hash	NOUN
fcis-22002	212	21	code	code	NOUN
fcis-22002	212	22	lengths	length	NOUN
fcis-22002	212	23	on	on	ADP
fcis-22002	212	24	the	the	DET
fcis-22002	212	25	model	model	NOUN
fcis-22002	212	26	performance	performance	NOUN
fcis-22002	212	27	,	,	PUNCT
fcis-22002	212	28	specifically	specifically	ADV
fcis-22002	212	29	examining	examine	VERB
fcis-22002	212	30	the	the	DET
fcis-22002	212	31	lengths	length	NOUN
fcis-22002	212	32	[	[	X
fcis-22002	212	33	512,640,768,896,1024	512,640,768,896,1024	NUM
fcis-22002	212	34	]	]	PUNCT
fcis-22002	212	35	.	.	PUNCT
fcis-22002	213	1	as	as	SCONJ
fcis-22002	213	2	analyzed	analyze	VERB
fcis-22002	213	3	in	in	ADP
fcis-22002	213	4	figure	figure	NOUN
fcis-22002	213	5	3.6	3.6	NUM
fcis-22002	213	6	,	,	PUNCT
fcis-22002	213	7	the	the	DET
fcis-22002	213	8	model	model	NOUN
fcis-22002	213	9	performance	performance	NOUN
fcis-22002	213	10	was	be	AUX
fcis-22002	213	11	significantly	significantly	ADV
fcis-22002	213	12	improved	improve	VERB
fcis-22002	213	13	when	when	SCONJ
fcis-22002	213	14	increasing	increase	VERB
fcis-22002	213	15	the	the	DET
fcis-22002	213	16	hash	hash	NOUN
fcis-22002	213	17	code	code	NOUN
fcis-22002	213	18	length	length	NOUN
fcis-22002	213	19	from	from	ADP
fcis-22002	213	20	512	512	NUM
fcis-22002	213	21	to	to	PART
fcis-22002	213	22	768	768	NUM
fcis-22002	213	23	bits	bit	NOUN
fcis-22002	213	24	.	.	PUNCT
fcis-22002	214	1	however	however	ADV
fcis-22002	214	2	,	,	PUNCT
fcis-22002	214	3	by	by	ADP
fcis-22002	214	4	continuing	continue	VERB
fcis-22002	214	5	to	to	PART
fcis-22002	214	6	increase	increase	VERB
fcis-22002	214	7	the	the	DET
fcis-22002	214	8	length	length	NOUN
fcis-22002	214	9	to	to	ADP
fcis-22002	214	10	1024	1024	NUM
fcis-22002	214	11	,	,	PUNCT
fcis-22002	214	12	the	the	DET
fcis-22002	214	13	retrieval	retrieval	NOUN
fcis-22002	214	14	accuracy	accuracy	NOUN
fcis-22002	214	15	starts	start	VERB
fcis-22002	214	16	to	to	PART
fcis-22002	214	17	fluctuate	fluctuate	VERB
fcis-22002	214	18	.	.	PUNCT
fcis-22002	214	19	table	table	NOUN
fcis-22002	214	20	6	6	NUM
fcis-22002	214	21	.	.	PUNCT
fcis-22002	215	1	effect	effect	NOUN
fcis-22002	215	2	of	of	ADP
fcis-22002	215	3	hash	hash	NOUN
fcis-22002	215	4	code	code	NOUN
fcis-22002	215	5	length	length	NOUN
fcis-22002	215	6	on	on	ADP
fcis-22002	215	7	retrieval	retrieval	NOUN
fcis-22002	215	8	performance	performance	NOUN
fcis-22002	215	9	hash	hash	NOUN
fcis-22002	215	10	code	code	NOUN
fcis-22002	215	11	length	length	NOUN
fcis-22002	215	12	retrieval	retrieval	NOUN
fcis-22002	215	13	of	of	ADP
fcis-22002	215	14	indicators	indicator	NOUN
fcis-22002	215	15	map	map	VERB
fcis-22002	215	16	mrr	mrr	PROPN
fcis-22002	215	17	p@1	p@1	PROPN
fcis-22002	215	18	p@5	p@5	PROPN
fcis-22002	215	19	p@9	p@9	PROPN
fcis-22002	216	1	512	512	NUM
fcis-22002	216	2	0.873	0.873	NUM
fcis-22002	216	3	0.967	0.967	NUM
fcis-22002	216	4	0.984	0.984	NUM
fcis-22002	216	5	0.966	0.966	NUM
fcis-22002	216	6	0.884	0.884	NUM
fcis-22002	216	7	640	640	NUM
fcis-22002	216	8	0.886	0.886	NUM
fcis-22002	216	9	0.952	0.952	NUM
fcis-22002	216	10	0.987	0.987	NUM
fcis-22002	216	11	0.971	0.971	NUM
fcis-22002	216	12	0.895	0.895	NUM
fcis-22002	216	13	768	768	NUM
fcis-22002	216	14	0.912	0.912	NUM
fcis-22002	216	15	0.984	0.984	NUM
fcis-22002	216	16	0.993	0.993	NUM
fcis-22002	216	17	0.981	0.981	NUM
fcis-22002	216	18	0.919	0.919	NUM
fcis-22002	216	19	896	896	NUM
fcis-22002	216	20	0.913	0.913	NUM
fcis-22002	216	21	0.983	0.983	NUM
fcis-22002	216	22	0.992	0.992	NUM
fcis-22002	216	23	0.983	0.983	NUM
fcis-22002	216	24	0.919	0.919	NUM
fcis-22002	216	25	1024	1024	NUM
fcis-22002	216	26	0.911	0.911	NUM
fcis-22002	216	27	0.988	0.988	NUM
fcis-22002	216	28	0.988	0.988	NUM
fcis-22002	216	29	0.979	0.979	NUM
fcis-22002	216	30	0.918	0.918	NUM
fcis-22002	216	31	5	5	NUM
fcis-22002	216	32	.	.	PUNCT
fcis-22002	216	33	conclusion	conclusion	NOUN
fcis-22002	216	34	in	in	ADP
fcis-22002	216	35	this	this	DET
fcis-22002	216	36	study	study	NOUN
fcis-22002	216	37	,	,	PUNCT
fcis-22002	216	38	we	we	PRON
fcis-22002	216	39	introduce	introduce	VERB
fcis-22002	216	40	a	a	DET
fcis-22002	216	41	novel	novel	ADJ
fcis-22002	216	42	end	end	NOUN
fcis-22002	216	43	-	-	PUNCT
fcis-22002	216	44	to	to	ADP
fcis-22002	216	45	-	-	PUNCT
fcis-22002	216	46	end	end	NOUN
fcis-22002	216	47	hashing	hashing	NOUN
fcis-22002	216	48	algorithm	algorithm	NOUN
fcis-22002	216	49	designed	design	VERB
fcis-22002	216	50	specifically	specifically	ADV
fcis-22002	216	51	for	for	ADP
fcis-22002	216	52	speech	speech	NOUN
fcis-22002	216	53	retrieval	retrieval	NOUN
fcis-22002	216	54	.	.	PUNCT
fcis-22002	217	1	unusually	unusually	ADV
fcis-22002	217	2	,	,	PUNCT
fcis-22002	217	3	the	the	DET
fcis-22002	217	4	algorithm	algorithm	NOUN
fcis-22002	217	5	bypasses	bypass	VERB
fcis-22002	217	6	the	the	DET
fcis-22002	217	7	traditional	traditional	ADJ
fcis-22002	217	8	transcription	transcription	NOUN
fcis-22002	217	9	phase	phase	NOUN
fcis-22002	217	10	.	.	PUNCT
fcis-22002	218	1	it	it	PRON
fcis-22002	218	2	directly	directly	ADV
fcis-22002	218	3	encodes	encode	VERB
fcis-22002	218	4	variable	variable	ADJ
fcis-22002	218	5	-	-	PUNCT
fcis-22002	218	6	length	length	NOUN
fcis-22002	218	7	speech	speech	NOUN
fcis-22002	218	8	content	content	NOUN
fcis-22002	218	9	at	at	ADP
fcis-22002	218	10	the	the	DET
fcis-22002	218	11	acoustic	acoustic	ADJ
fcis-22002	218	12	level	level	NOUN
fcis-22002	218	13	into	into	ADP
fcis-22002	218	14	a	a	DET
fcis-22002	218	15	uniform	uniform	ADJ
fcis-22002	218	16	-	-	PUNCT
fcis-22002	218	17	length	length	NOUN
fcis-22002	218	18	hash	hash	NOUN
fcis-22002	218	19	code	code	NOUN
fcis-22002	218	20	,	,	PUNCT
fcis-22002	218	21	thus	thus	ADV
fcis-22002	218	22	eliminating	eliminate	VERB
fcis-22002	218	23	the	the	DET
fcis-22002	218	24	reliance	reliance	NOUN
fcis-22002	218	25	on	on	ADP
fcis-22002	218	26	asr	asr	PROPN
fcis-22002	218	27	accuracy	accuracy	NOUN
fcis-22002	218	28	.	.	PUNCT
fcis-22002	219	1	our	our	PRON
fcis-22002	219	2	comprehensive	comprehensive	ADJ
fcis-22002	219	3	evaluation	evaluation	NOUN
fcis-22002	219	4	highly	highly	ADV
fcis-22002	219	5	highlights	highlight	VERB
fcis-22002	219	6	the	the	DET
fcis-22002	219	7	crucial	crucial	ADJ
fcis-22002	219	8	role	role	NOUN
fcis-22002	219	9	of	of	ADP
fcis-22002	219	10	the	the	DET
fcis-22002	219	11	pre	pre	NOUN
fcis-22002	219	12	-	-	NOUN
fcis-22002	219	13	training	training	NOUN
fcis-22002	219	14	,	,	PUNCT
fcis-22002	219	15	downsampling	downsampling	NOUN
fcis-22002	219	16	and	and	CCONJ
fcis-22002	219	17	sequence	sequence	NOUN
fcis-22002	219	18	matching	matching	NOUN
fcis-22002	219	19	encoder	encoder	NOUN
fcis-22002	219	20	modules	module	NOUN
fcis-22002	219	21	in	in	ADP
fcis-22002	219	22	the	the	DET
fcis-22002	219	23	algorithm	algorithm	NOUN
fcis-22002	219	24	.	.	PUNCT
fcis-22002	220	1	moreover	moreover	ADV
fcis-22002	220	2	,	,	PUNCT
fcis-22002	220	3	the	the	DET
fcis-22002	220	4	adaptability	adaptability	NOUN
fcis-22002	220	5	and	and	CCONJ
fcis-22002	220	6	excellent	excellent	ADJ
fcis-22002	220	7	performance	performance	NOUN
fcis-22002	220	8	of	of	ADP
fcis-22002	220	9	the	the	DET
fcis-22002	220	10	algorithm	algorithm	NOUN
fcis-22002	220	11	under	under	ADP
fcis-22002	220	12	various	various	ADJ
fcis-22002	220	13	noise	noise	NOUN
fcis-22002	220	14	conditions	condition	NOUN
fcis-22002	220	15	make	make	VERB
fcis-22002	220	16	it	it	PRON
fcis-22002	220	17	well	well	ADV
fcis-22002	220	18	suited	suited	ADJ
fcis-22002	220	19	for	for	ADP
fcis-22002	220	20	retrieval	retrieval	NOUN
fcis-22002	220	21	tasks	task	NOUN
fcis-22002	220	22	.	.	PUNCT
fcis-22002	221	1	the	the	DET
fcis-22002	221	2	results	result	NOUN
fcis-22002	221	3	of	of	ADP
fcis-22002	221	4	the	the	DET
fcis-22002	221	5	study	study	NOUN
fcis-22002	221	6	show	show	VERB
fcis-22002	221	7	that	that	SCONJ
fcis-22002	221	8	the	the	DET
fcis-22002	221	9	map	map	NOUN
fcis-22002	221	10	metrics	metric	NOUN
fcis-22002	221	11	of	of	ADP
fcis-22002	221	12	the	the	DET
fcis-22002	221	13	proposed	propose	VERB
fcis-22002	221	14	method	method	NOUN
fcis-22002	221	15	are	be	AUX
fcis-22002	221	16	well	well	ADV
fcis-22002	221	17	adapted	adapt	VERB
fcis-22002	221	18	,	,	PUNCT
fcis-22002	221	19	emphasizing	emphasize	VERB
fcis-22002	221	20	its	its	PRON
fcis-22002	221	21	capability	capability	NOUN
fcis-22002	221	22	in	in	ADP
fcis-22002	221	23	speech	speech	NOUN
fcis-22002	221	24	28	28	NUM
fcis-22002	221	25	retrieval	retrieval	NOUN
fcis-22002	221	26	applications	application	NOUN
fcis-22002	221	27	.	.	PUNCT
fcis-22002	222	1	in	in	ADP
fcis-22002	222	2	the	the	DET
fcis-22002	222	3	future	future	NOUN
fcis-22002	222	4	,	,	PUNCT
fcis-22002	222	5	in	in	ADP
fcis-22002	222	6	addition	addition	NOUN
fcis-22002	222	7	to	to	ADP
fcis-22002	222	8	using	use	VERB
fcis-22002	222	9	text	text	NOUN
fcis-22002	222	10	-	-	PUNCT
fcis-22002	222	11	labeled	label	VERB
fcis-22002	222	12	datasets	dataset	NOUN
fcis-22002	222	13	,	,	PUNCT
fcis-22002	222	14	we	we	PRON
fcis-22002	222	15	will	will	AUX
fcis-22002	222	16	apply	apply	VERB
fcis-22002	222	17	our	our	PRON
fcis-22002	222	18	algorithm	algorithm	NOUN
fcis-22002	222	19	to	to	ADP
fcis-22002	222	20	unlabeled	unlabele	VERB
fcis-22002	222	21	datasets	dataset	NOUN
fcis-22002	222	22	.	.	PUNCT
fcis-22002	223	1	we	we	PRON
fcis-22002	223	2	will	will	AUX
fcis-22002	223	3	use	use	VERB
fcis-22002	223	4	an	an	DET
fcis-22002	223	5	unsupervised	unsupervised	ADJ
fcis-22002	223	6	approach	approach	NOUN
fcis-22002	223	7	to	to	ADP
fcis-22002	223	8	facilitate	facilitate	VERB
fcis-22002	223	9	speech	speech	NOUN
fcis-22002	223	10	retrieval	retrieval	NOUN
fcis-22002	223	11	in	in	ADP
fcis-22002	223	12	situations	situation	NOUN
fcis-22002	223	13	where	where	SCONJ
fcis-22002	223	14	languages	language	NOUN
fcis-22002	223	15	are	be	AUX
fcis-22002	223	16	poorly	poorly	ADV
fcis-22002	223	17	known	know	VERB
fcis-22002	223	18	and	and	CCONJ
fcis-22002	223	19	resources	resource	NOUN
fcis-22002	223	20	are	be	AUX
fcis-22002	223	21	limited	limited	ADJ
fcis-22002	223	22	.	.	PUNCT
fcis-22002	224	1	references	reference	NOUN
fcis-22002	224	2	[	[	X
fcis-22002	224	3	1	1	NUM
fcis-22002	224	4	]	]	PUNCT
fcis-22002	224	5	m.	m.	PROPN
fcis-22002	224	6	b.	b.	PROPN
fcis-22002	224	7	ak¸cay	ak¸cay	PROPN
fcis-22002	224	8	and	and	CCONJ
fcis-22002	224	9	k.	k.	PROPN
fcis-22002	224	10	o˘	o˘	PROPN
fcis-22002	224	11	guz	guz	PROPN
fcis-22002	224	12	,	,	PUNCT
fcis-22002	224	13	“	"	PUNCT
fcis-22002	224	14	speech	speech	NOUN
fcis-22002	224	15	emotion	emotion	NOUN
fcis-22002	224	16	recognition	recognition	NOUN
fcis-22002	224	17	:	:	PUNCT
fcis-22002	224	18	emotional	emotional	ADJ
fcis-22002	224	19	models	model	NOUN
fcis-22002	224	20	,	,	PUNCT
fcis-22002	224	21	databases	database	NOUN
fcis-22002	224	22	,	,	PUNCT
fcis-22002	224	23	features	feature	NOUN
fcis-22002	224	24	,	,	PUNCT
fcis-22002	224	25	preprocessing	preprocesse	VERB
fcis-22002	224	26	methods	method	NOUN
fcis-22002	224	27	,	,	PUNCT
fcis-22002	224	28	supporting	support	VERB
fcis-22002	224	29	modal	modal	ADJ
fcis-22002	224	30	ities	itie	NOUN
fcis-22002	224	31	,	,	PUNCT
fcis-22002	224	32	and	and	CCONJ
fcis-22002	224	33	classifiers	classifier	NOUN
fcis-22002	224	34	,	,	PUNCT
fcis-22002	224	35	”	"	PUNCT
fcis-22002	224	36	speech	speech	NOUN
fcis-22002	224	37	communication	communication	NOUN
fcis-22002	224	38	,	,	PUNCT
fcis-22002	224	39	vol	vol	NOUN
fcis-22002	224	40	.	.	PROPN
fcis-22002	224	41	116	116	NUM
fcis-22002	224	42	,	,	PUNCT
fcis-22002	224	43	pp	pp	ADJ
fcis-22002	224	44	.	.	PUNCT
fcis-22002	224	45	56–76	56–76	NUM
fcis-22002	224	46	,	,	PUNCT
fcis-22002	224	47	2020	2020	NUM
fcis-22002	224	48	.	.	PUNCT
fcis-22002	225	1	w.-k	w.-k	PROPN
fcis-22002	225	2	.	.	PUNCT
fcis-22002	226	1	chen	chen	PROPN
fcis-22002	226	2	,	,	PUNCT
fcis-22002	226	3	linear	linear	ADJ
fcis-22002	226	4	networks	network	NOUN
fcis-22002	226	5	and	and	CCONJ
fcis-22002	226	6	systems	system	NOUN
fcis-22002	226	7	(	(	PUNCT
fcis-22002	226	8	book	book	NOUN
fcis-22002	226	9	style	style	NOUN
fcis-22002	226	10	)	)	PUNCT
fcis-22002	226	11	.	.	PUNCT
fcis-22002	227	1	belmont	belmont	PROPN
fcis-22002	227	2	,	,	PUNCT
fcis-22002	227	3	ca	can	AUX
fcis-22002	227	4	:	:	PUNCT
fcis-22002	227	5	wadsworth	wadsworth	NOUN
fcis-22002	227	6	,	,	PUNCT
fcis-22002	227	7	1993	1993	NUM
fcis-22002	227	8	,	,	PUNCT
fcis-22002	227	9	pp	pp	ADP
fcis-22002	227	10	.	.	PUNCT
fcis-22002	228	1	123–135	123–135	NUM
fcis-22002	228	2	.	.	PUNCT
fcis-22002	229	1	[	[	X
fcis-22002	229	2	2	2	NUM
fcis-22002	229	3	]	]	PUNCT
fcis-22002	229	4	l.-s	l.-	NOUN
fcis-22002	229	5	.	.	PUNCT
fcis-22002	230	1	lee	lee	PROPN
fcis-22002	230	2	,	,	PUNCT
fcis-22002	230	3	j.	j.	PROPN
fcis-22002	230	4	glass	glass	PROPN
fcis-22002	230	5	,	,	PUNCT
fcis-22002	230	6	h.-y	h.-y	NOUN
fcis-22002	230	7	.	.	PUNCT
fcis-22002	231	1	lee	lee	PROPN
fcis-22002	231	2	,	,	PUNCT
fcis-22002	231	3	and	and	CCONJ
fcis-22002	231	4	c.-a	c.-a	NOUN
fcis-22002	231	5	.	.	PUNCT
fcis-22002	232	1	chan	chan	PROPN
fcis-22002	232	2	,	,	PUNCT
fcis-22002	232	3	“	"	PUNCT
fcis-22002	232	4	spoken	spoken	ADJ
fcis-22002	232	5	content	content	NOUN
fcis-22002	232	6	retrieval	retrieval	NOUN
fcis-22002	232	7	—	—	PUNCT
fcis-22002	232	8	beyond	beyond	ADP
fcis-22002	232	9	cascading	cascade	VERB
fcis-22002	232	10	speech	speech	NOUN
fcis-22002	232	11	recognition	recognition	NOUN
fcis-22002	232	12	with	with	ADP
fcis-22002	232	13	text	text	NOUN
fcis-22002	232	14	retrieval	retrieval	NOUN
fcis-22002	232	15	,	,	PUNCT
fcis-22002	232	16	”	"	PUNCT
fcis-22002	232	17	ieee	ieee	NOUN
fcis-22002	232	18	/	/	SYM
fcis-22002	232	19	acm	acm	NOUN
fcis-22002	232	20	transactions	transaction	NOUN
fcis-22002	232	21	on	on	ADP
fcis-22002	232	22	audio	audio	NOUN
fcis-22002	232	23	,	,	PUNCT
fcis-22002	232	24	speech	speech	NOUN
fcis-22002	232	25	,	,	PUNCT
fcis-22002	232	26	and	and	CCONJ
fcis-22002	232	27	language	language	NOUN
fcis-22002	232	28	processing	processing	NOUN
fcis-22002	232	29	,	,	PUNCT
fcis-22002	232	30	vol	vol	NOUN
fcis-22002	232	31	.	.	PROPN
fcis-22002	232	32	23	23	NUM
fcis-22002	232	33	,	,	PUNCT
fcis-22002	232	34	no	no	INTJ
fcis-22002	232	35	.	.	NOUN
fcis-22002	232	36	9	9	NUM
fcis-22002	232	37	,	,	PUNCT
fcis-22002	232	38	pp	pp	ADJ
fcis-22002	232	39	.	.	PUNCT
fcis-22002	232	40	1389–1420	1389–1420	NUM
fcis-22002	232	41	,	,	PUNCT
fcis-22002	232	42	2015	2015	NUM
fcis-22002	232	43	b.	b.	PROPN
fcis-22002	232	44	smith	smith	PROPN
fcis-22002	232	45	,	,	PUNCT
fcis-22002	232	46	“	"	PUNCT
fcis-22002	232	47	an	an	DET
fcis-22002	232	48	approach	approach	NOUN
fcis-22002	232	49	to	to	ADP
fcis-22002	232	50	graphs	graph	NOUN
fcis-22002	232	51	of	of	ADP
fcis-22002	232	52	linear	linear	ADJ
fcis-22002	232	53	forms	form	NOUN
fcis-22002	232	54	(	(	PUNCT
fcis-22002	232	55	unpublished	unpublished	ADJ
fcis-22002	232	56	work	work	NOUN
fcis-22002	232	57	style	style	NOUN
fcis-22002	232	58	)	)	PUNCT
fcis-22002	232	59	,	,	PUNCT
fcis-22002	232	60	”	"	PUNCT
fcis-22002	232	61	unpublished	unpublished	ADJ
fcis-22002	232	62	.	.	PUNCT
fcis-22002	233	1	[	[	X
fcis-22002	233	2	3	3	NUM
fcis-22002	233	3	]	]	SYM
fcis-22002	233	4	y.-b	y.-b	PROPN
fcis-22002	233	5	.	.	PUNCT
fcis-22002	233	6	huang	huang	PROPN
fcis-22002	233	7	,	,	PUNCT
fcis-22002	233	8	y.	y.	PROPN
fcis-22002	233	9	wang	wang	PROPN
fcis-22002	233	10	,	,	PUNCT
fcis-22002	233	11	h.	h.	PROPN
fcis-22002	233	12	li	li	PROPN
fcis-22002	233	13	,	,	PUNCT
fcis-22002	233	14	y.	y.	PROPN
fcis-22002	233	15	zhang	zhang	PROPN
fcis-22002	233	16	,	,	PUNCT
fcis-22002	233	17	and	and	CCONJ
fcis-22002	233	18	q.-y	q.-y	NOUN
fcis-22002	233	19	.	.	PUNCT
fcis-22002	234	1	zhang	zhang	PROPN
fcis-22002	234	2	,	,	PUNCT
fcis-22002	234	3	“	"	PUNCT
fcis-22002	234	4	encrypted	encrypt	VERB
fcis-22002	234	5	speech	speech	NOUN
fcis-22002	234	6	retrieval	retrieval	NOUN
fcis-22002	234	7	based	base	VERB
fcis-22002	234	8	on	on	ADP
fcis-22002	234	9	long	long	ADJ
fcis-22002	234	10	sequence	sequence	NOUN
fcis-22002	234	11	biohashing	biohashing	NOUN
fcis-22002	234	12	,	,	PUNCT
fcis-22002	234	13	”	"	PUNCT
fcis-22002	234	14	multimedia	multimedia	NOUN
fcis-22002	234	15	tools	tool	NOUN
fcis-22002	234	16	and	and	CCONJ
fcis-22002	234	17	applications	application	NOUN
fcis-22002	234	18	,	,	PUNCT
fcis-22002	234	19	vol	vol	NOUN
fcis-22002	234	20	.	.	PROPN
fcis-22002	234	21	81	81	NUM
fcis-22002	234	22	,	,	PUNCT
fcis-22002	234	23	no	no	INTJ
fcis-22002	234	24	.	.	NOUN
fcis-22002	234	25	9	9	NUM
fcis-22002	234	26	,	,	PUNCT
fcis-22002	234	27	pp	pp	ADJ
fcis-22002	234	28	.	.	PUNCT
fcis-22002	234	29	13065–13085	13065–13085	NUM
fcis-22002	234	30	,	,	PUNCT
fcis-22002	234	31	2022	2022	NUM
fcis-22002	234	32	.	.	PUNCT
fcis-22002	235	1	j.	j.	PROPN
fcis-22002	235	2	wang	wang	PROPN
fcis-22002	235	3	,	,	PUNCT
fcis-22002	235	4	“	"	PUNCT
fcis-22002	235	5	fundamentals	fundamental	NOUN
fcis-22002	235	6	of	of	ADP
fcis-22002	235	7	erbiumdoped	erbiumdope	VERB
fcis-22002	235	8	fiber	fiber	NOUN
fcis-22002	235	9	amplifiers	amplifier	NOUN
fcis-22002	235	10	arrays	array	VERB
fcis-22002	235	11	(	(	PUNCT
fcis-22002	235	12	periodical	periodical	ADJ
fcis-22002	235	13	style	style	NOUN
fcis-22002	235	14	—	—	PUNCT
fcis-22002	235	15	submitted	submit	VERB
fcis-22002	235	16	for	for	ADP
fcis-22002	235	17	publication	publication	NOUN
fcis-22002	235	18	)	)	PUNCT
fcis-22002	235	19	,	,	PUNCT
fcis-22002	235	20	”	"	PUNCT
fcis-22002	235	21	ieee	ieee	PROPN
fcis-22002	235	22	j.	j.	PROPN
fcis-22002	235	23	quantum	quantum	PROPN
fcis-22002	235	24	electron	electron	PROPN
fcis-22002	235	25	.	.	PUNCT
fcis-22002	235	26	,	,	PUNCT
fcis-22002	235	27	submitted	submit	VERB
fcis-22002	235	28	for	for	ADP
fcis-22002	235	29	publication	publication	NOUN
fcis-22002	235	30	.	.	PUNCT
fcis-22002	236	1	[	[	X
fcis-22002	236	2	4	4	X
fcis-22002	236	3	]	]	X
fcis-22002	236	4	w.	w.	PROPN
fcis-22002	236	5	khan	khan	PROPN
fcis-22002	236	6	and	and	CCONJ
fcis-22002	236	7	k.	k.	PROPN
fcis-22002	236	8	kuru	kuru	PROPN
fcis-22002	236	9	,	,	PUNCT
fcis-22002	236	10	“	"	PUNCT
fcis-22002	236	11	an	an	DET
fcis-22002	236	12	intelligent	intelligent	ADJ
fcis-22002	236	13	system	system	NOUN
fcis-22002	236	14	for	for	ADP
fcis-22002	236	15	spoken	spoken	ADJ
fcis-22002	236	16	term	term	NOUN
fcis-22002	236	17	detection	detection	NOUN
fcis-22002	236	18	that	that	PRON
fcis-22002	236	19	uses	use	VERB
fcis-22002	236	20	belief	belief	NOUN
fcis-22002	236	21	combination	combination	NOUN
fcis-22002	236	22	,	,	PUNCT
fcis-22002	236	23	”	"	PUNCT
fcis-22002	236	24	ieee	ieee	NOUN
fcis-22002	236	25	intelligent	intelligent	ADJ
fcis-22002	236	26	systems	system	NOUN
fcis-22002	236	27	,	,	PUNCT
fcis-22002	236	28	vol	vol	NOUN
fcis-22002	236	29	.	.	PROPN
fcis-22002	237	1	32	32	NUM
fcis-22002	237	2	,	,	PUNCT
fcis-22002	237	3	no	no	INTJ
fcis-22002	237	4	.	.	NOUN
fcis-22002	237	5	1	1	NUM
fcis-22002	237	6	,	,	PUNCT
fcis-22002	237	7	p.	p.	NOUN
fcis-22002	237	8	70–79	70–79	NOUN
fcis-22002	237	9	,	,	PUNCT
fcis-22002	237	10	feb	feb	PROPN
fcis-22002	237	11	2017	2017	NUM
fcis-22002	237	12	.	.	PUNCT
fcis-22002	238	1	[	[	X
fcis-22002	238	2	online	online	X
fcis-22002	238	3	]	]	X
fcis-22002	238	4	.	.	PUNCT
fcis-22002	239	1	available	available	ADJ
fcis-22002	239	2	:	:	PUNCT
fcis-22002	239	3	y.	y.	PROPN
fcis-22002	239	4	yorozu	yorozu	PROPN
fcis-22002	239	5	,	,	PUNCT
fcis-22002	239	6	m.	m.	PROPN
fcis-22002	239	7	hirano	hirano	PROPN
fcis-22002	239	8	,	,	PUNCT
fcis-22002	239	9	k.	k.	PROPN
fcis-22002	239	10	oka	oka	PROPN
fcis-22002	239	11	,	,	PUNCT
fcis-22002	239	12	and	and	CCONJ
fcis-22002	239	13	y.	y.	PROPN
fcis-22002	239	14	tagawa	tagawa	PROPN
fcis-22002	239	15	,	,	PUNCT
fcis-22002	239	16	“	"	PUNCT
fcis-22002	239	17	electron	electron	NOUN
fcis-22002	239	18	spectroscopy	spectroscopy	NOUN
fcis-22002	239	19	studies	study	NOUN
fcis-22002	239	20	on	on	ADP
fcis-22002	239	21	magneto	magneto	ADJ
fcis-22002	239	22	-	-	PUNCT
fcis-22002	239	23	optical	optical	ADJ
fcis-22002	239	24	media	medium	NOUN
fcis-22002	239	25	and	and	CCONJ
fcis-22002	239	26	plastic	plastic	NOUN
fcis-22002	239	27	substrate	substrate	NOUN
fcis-22002	239	28	interfaces	interface	NOUN
fcis-22002	239	29	(	(	PUNCT
fcis-22002	239	30	translation	translation	NOUN
fcis-22002	239	31	journals	journal	NOUN
fcis-22002	239	32	style	style	NOUN
fcis-22002	239	33	)	)	PUNCT
fcis-22002	239	34	,	,	PUNCT
fcis-22002	239	35	”	"	PUNCT
fcis-22002	239	36	ieee	ieee	NOUN
fcis-22002	239	37	transl	transl	PROPN
fcis-22002	239	38	.	.	PUNCT
fcis-22002	240	1	j.	j.	PROPN
fcis-22002	240	2	magn	magn	PROPN
fcis-22002	240	3	.	.	PUNCT
fcis-22002	241	1	jpn	jpn	PROPN
fcis-22002	241	2	.	.	PROPN
fcis-22002	241	3	,	,	PUNCT
fcis-22002	241	4	vol	vol	NOUN
fcis-22002	241	5	.	.	PROPN
fcis-22002	241	6	2	2	NUM
fcis-22002	241	7	,	,	PUNCT
fcis-22002	241	8	aug	aug	PROPN
fcis-22002	241	9	.	.	PROPN
fcis-22002	241	10	1987	1987	NUM
fcis-22002	241	11	,	,	PUNCT
fcis-22002	241	12	pp	pp	ADP
fcis-22002	241	13	.	.	PUNCT
fcis-22002	242	1	740–741	740–741	NUM
fcis-22002	242	2	[	[	X
fcis-22002	242	3	dig	dig	X
fcis-22002	242	4	.	.	PUNCT
fcis-22002	243	1	9th	9th	ADJ
fcis-22002	243	2	annu	annu	PROPN
fcis-22002	243	3	.	.	PUNCT
fcis-22002	243	4	conf	conf	NOUN
fcis-22002	243	5	.	.	PUNCT
fcis-22002	244	1	magnetics	magnetic	NOUN
fcis-22002	244	2	japan	japan	PROPN
fcis-22002	244	3	,	,	PUNCT
fcis-22002	244	4	1982	1982	NUM
fcis-22002	244	5	,	,	PUNCT
fcis-22002	244	6	p.	p.	NOUN
fcis-22002	244	7	301	301	NUM
fcis-22002	244	8	]	]	PUNCT
fcis-22002	244	9	.	.	PUNCT
fcis-22002	245	1	[	[	X
fcis-22002	245	2	5	5	X
fcis-22002	245	3	]	]	PUNCT
fcis-22002	245	4	t.	t.	PROPN
fcis-22002	245	5	k.	k.	PROPN
fcis-22002	245	6	chia	chia	PROPN
fcis-22002	245	7	,	,	PUNCT
fcis-22002	245	8	k.	k.	PROPN
fcis-22002	245	9	c.	c.	PROPN
fcis-22002	245	10	sim	sim	PROPN
fcis-22002	245	11	,	,	PUNCT
fcis-22002	245	12	h.	h.	PROPN
fcis-22002	245	13	li	li	PROPN
fcis-22002	245	14	,	,	PUNCT
fcis-22002	245	15	and	and	CCONJ
fcis-22002	245	16	h.	h.	PROPN
fcis-22002	245	17	t.	t.	PROPN
fcis-22002	245	18	ng	ng	PROPN
fcis-22002	245	19	,	,	PUNCT
fcis-22002	245	20	“	"	PUNCT
fcis-22002	245	21	a	a	DET
fcis-22002	245	22	lattice	lattice	NOUN
fcis-22002	245	23	-	-	PUNCT
fcis-22002	245	24	based	base	VERB
fcis-22002	245	25	approach	approach	NOUN
fcis-22002	245	26	to	to	ADP
fcis-22002	245	27	query	query	NOUN
fcis-22002	245	28	-	-	PUNCT
fcis-22002	245	29	by	by	ADP
fcis-22002	245	30	-	-	PUNCT
fcis-22002	245	31	example	example	NOUN
fcis-22002	245	32	spoken	speak	VERB
fcis-22002	245	33	document	document	NOUN
fcis-22002	245	34	retrieval	retrieval	NOUN
fcis-22002	245	35	,	,	PUNCT
fcis-22002	245	36	”	"	PUNCT
fcis-22002	245	37	in	in	ADP
fcis-22002	245	38	proceedings	proceeding	NOUN
fcis-22002	245	39	of	of	ADP
fcis-22002	245	40	the	the	DET
fcis-22002	245	41	j.	j.	PROPN
fcis-22002	245	42	u.	u.	PROPN
fcis-22002	245	43	duncombe	duncombe	PROPN
fcis-22002	245	44	,	,	PUNCT
fcis-22002	245	45	“	"	PUNCT
fcis-22002	245	46	infrared	infrared	ADJ
fcis-22002	245	47	navigation	navigation	NOUN
fcis-22002	245	48	—	—	PUNCT
fcis-22002	245	49	part	part	NOUN
fcis-22002	245	50	i	i	NOUN
fcis-22002	245	51	:	:	PUNCT
fcis-22002	245	52	an	an	DET
fcis-22002	245	53	assessment	assessment	NOUN
fcis-22002	245	54	of	of	ADP
fcis-22002	245	55	feasibility	feasibility	NOUN
fcis-22002	245	56	(	(	PUNCT
fcis-22002	245	57	periodical	periodical	ADJ
fcis-22002	245	58	style	style	NOUN
fcis-22002	245	59	)	)	PUNCT
fcis-22002	245	60	,	,	PUNCT
fcis-22002	245	61	”	"	PUNCT
fcis-22002	245	62	ieee	ieee	NOUN
fcis-22002	245	63	trans	trans	PROPN
fcis-22002	245	64	.	.	PROPN
fcis-22002	245	65	electron	electron	PROPN
fcis-22002	245	66	devices	device	NOUN
fcis-22002	245	67	,	,	PUNCT
fcis-22002	245	68	vol	vol	NOUN
fcis-22002	245	69	.	.	PUNCT
fcis-22002	245	70	ed-11	ed-11	ADV
fcis-22002	245	71	,	,	PUNCT
fcis-22002	245	72	pp	pp	ADJ
fcis-22002	245	73	.	.	PUNCT
fcis-22002	246	1	34–39	34–39	NUM
fcis-22002	246	2	,	,	PUNCT
fcis-22002	246	3	jan	jan	PROPN
fcis-22002	246	4	.	.	PROPN
fcis-22002	246	5	1959	1959	NUM
fcis-22002	246	6	.	.	PUNCT
fcis-22002	247	1	31st	31st	ADJ
fcis-22002	247	2	annual	annual	ADJ
fcis-22002	247	3	international	international	PROPN
fcis-22002	247	4	acm	acm	PROPN
fcis-22002	247	5	sigir	sigir	PROPN
fcis-22002	247	6	conference	conference	PROPN
fcis-22002	247	7	on	on	ADP
fcis-22002	247	8	research	research	NOUN
fcis-22002	247	9	and	and	CCONJ
fcis-22002	247	10	de	de	X
fcis-22002	247	11	velopment	velopment	NOUN
fcis-22002	247	12	in	in	ADP
fcis-22002	247	13	information	information	NOUN
fcis-22002	247	14	retrieval	retrieval	NOUN
fcis-22002	247	15	,	,	PUNCT
fcis-22002	247	16	2008	2008	NUM
fcis-22002	247	17	,	,	PUNCT
fcis-22002	247	18	pp	pp	ADJ
fcis-22002	247	19	.	.	PUNCT
fcis-22002	248	1	363–370	363–370	NUM
fcis-22002	248	2	.	.	PUNCT
fcis-22002	249	1	[	[	X
fcis-22002	249	2	6	6	NUM
fcis-22002	249	3	]	]	X
fcis-22002	249	4	c.	c.	PROPN
fcis-22002	249	5	parada	parada	PROPN
fcis-22002	249	6	,	,	PUNCT
fcis-22002	249	7	a.	a.	NOUN
fcis-22002	249	8	sethy	sethy	ADJ
fcis-22002	249	9	,	,	PUNCT
fcis-22002	249	10	and	and	CCONJ
fcis-22002	249	11	b.	b.	PROPN
fcis-22002	249	12	ramabhadran	ramabhadran	PROPN
fcis-22002	249	13	,	,	PUNCT
fcis-22002	249	14	“	"	PUNCT
fcis-22002	249	15	query	query	NOUN
fcis-22002	249	16	-	-	PUNCT
fcis-22002	249	17	by	by	ADP
fcis-22002	249	18	-	-	PUNCT
fcis-22002	249	19	example	example	NOUN
fcis-22002	249	20	spoken	spoken	ADJ
fcis-22002	249	21	term	term	NOUN
fcis-22002	249	22	detection	detection	NOUN
fcis-22002	249	23	for	for	ADP
fcis-22002	249	24	oov	oov	PROPN
fcis-22002	249	25	terms	term	NOUN
fcis-22002	249	26	,	,	PUNCT
fcis-22002	249	27	”	"	PUNCT
fcis-22002	249	28	in	in	ADP
fcis-22002	249	29	2009	2009	NUM
fcis-22002	249	30	ieee	ieee	NOUN
fcis-22002	249	31	workshop	workshop	NOUN
fcis-22002	249	32	on	on	ADP
fcis-22002	249	33	automatic	automatic	ADJ
fcis-22002	249	34	speech	speech	NOUN
fcis-22002	249	35	recognition	recognition	NOUN
fcis-22002	249	36	&	&	CCONJ
fcis-22002	249	37	understanding	understanding	NOUN
fcis-22002	249	38	.	.	PUNCT
fcis-22002	250	1	ieee	ieee	NOUN
fcis-22002	250	2	,	,	PUNCT
fcis-22002	250	3	2009	2009	NUM
fcis-22002	250	4	,	,	PUNCT
fcis-22002	250	5	pp	pp	ADP
fcis-22002	250	6	.	.	PUNCT
fcis-22002	251	1	404–409	404–409	NUM
fcis-22002	251	2	.	.	PUNCT
fcis-22002	251	3	r.	r.	PROPN
fcis-22002	251	4	w.	w.	PROPN
fcis-22002	251	5	lucky	lucky	PROPN
fcis-22002	251	6	,	,	PUNCT
fcis-22002	251	7	“	"	PUNCT
fcis-22002	251	8	automatic	automatic	ADJ
fcis-22002	251	9	equalization	equalization	NOUN
fcis-22002	251	10	for	for	ADP
fcis-22002	251	11	digital	digital	ADJ
fcis-22002	251	12	communication	communication	NOUN
fcis-22002	251	13	,	,	PUNCT
fcis-22002	251	14	”	"	PUNCT
fcis-22002	251	15	bell	bell	NOUN
fcis-22002	251	16	syst	syst	NOUN
fcis-22002	251	17	.	.	PUNCT
fcis-22002	252	1	tech	tech	PROPN
fcis-22002	252	2	.	.	PUNCT
fcis-22002	253	1	j.	j.	PROPN
fcis-22002	253	2	,	,	PUNCT
fcis-22002	253	3	vol	vol	NOUN
fcis-22002	253	4	.	.	PROPN
fcis-22002	253	5	44	44	NUM
fcis-22002	253	6	,	,	PUNCT
fcis-22002	253	7	no	no	INTJ
fcis-22002	253	8	.	.	NOUN
fcis-22002	253	9	4	4	NUM
fcis-22002	253	10	,	,	PUNCT
fcis-22002	253	11	pp	pp	ADJ
fcis-22002	253	12	.	.	PUNCT
fcis-22002	254	1	547–588	547–588	NUM
fcis-22002	254	2	,	,	PUNCT
fcis-22002	254	3	apr	apr	PROPN
fcis-22002	254	4	.	.	PUNCT
fcis-22002	254	5	1965	1965	NUM
fcis-22002	254	6	.	.	PUNCT
fcis-22002	255	1	[	[	X
fcis-22002	255	2	7	7	X
fcis-22002	255	3	]	]	X
fcis-22002	255	4	y.	y.	NOUN
fcis-22002	255	5	moriya	moriya	PROPN
fcis-22002	255	6	and	and	CCONJ
fcis-22002	255	7	g.	g.	PROPN
fcis-22002	255	8	j.	j.	PROPN
fcis-22002	255	9	jones	jones	PROPN
fcis-22002	255	10	,	,	PUNCT
fcis-22002	255	11	“	"	PUNCT
fcis-22002	255	12	improving	improve	VERB
fcis-22002	255	13	noise	noise	NOUN
fcis-22002	255	14	robustness	robustness	NOUN
fcis-22002	255	15	for	for	ADP
fcis-22002	255	16	spoken	spoken	ADJ
fcis-22002	255	17	content	content	NOUN
fcis-22002	255	18	retrieval	retrieval	NOUN
fcis-22002	255	19	using	use	VERB
fcis-22002	255	20	semi	semi	ADJ
fcis-22002	255	21	-	-	ADJ
fcis-22002	255	22	supervised	supervised	ADJ
fcis-22002	255	23	asr	asr	NOUN
fcis-22002	255	24	and	and	CCONJ
fcis-22002	255	25	n	n	CCONJ
fcis-22002	255	26	-	-	PUNCT
fcis-22002	255	27	best	good	ADJ
fcis-22002	255	28	transcripts	transcript	NOUN
fcis-22002	255	29	for	for	ADP
fcis-22002	255	30	bert	bert	NOUN
fcis-22002	255	31	-	-	PUNCT
fcis-22002	255	32	based	base	VERB
fcis-22002	255	33	ranking	ranking	NOUN
fcis-22002	255	34	models	model	NOUN
fcis-22002	255	35	,	,	PUNCT
fcis-22002	255	36	”	"	PUNCT
fcis-22002	255	37	in	in	ADP
fcis-22002	255	38	2022	2022	NUM
fcis-22002	255	39	ieee	ieee	NOUN
fcis-22002	255	40	spoken	speak	VERB
fcis-22002	255	41	language	language	NOUN
fcis-22002	255	42	technology	technology	NOUN
fcis-22002	255	43	workshop	workshop	NOUN
fcis-22002	255	44	(	(	PUNCT
fcis-22002	255	45	slt	slt	PROPN
fcis-22002	255	46	)	)	PUNCT
fcis-22002	255	47	.	.	PUNCT
fcis-22002	256	1	ieee	ieee	NOUN
fcis-22002	256	2	,	,	PUNCT
fcis-22002	256	3	2023	2023	NUM
fcis-22002	256	4	,	,	PUNCT
fcis-22002	256	5	pp	pp	ADJ
fcis-22002	256	6	.	.	PUNCT
fcis-22002	257	1	398–405	398–405	NUM
fcis-22002	257	2	.	.	PUNCT
fcis-22002	258	1	g.	g.	PROPN
fcis-22002	258	2	r.	r.	PROPN
fcis-22002	258	3	faulhaber	faulhaber	PROPN
fcis-22002	258	4	,	,	PUNCT
fcis-22002	258	5	“	"	PUNCT
fcis-22002	258	6	design	design	NOUN
fcis-22002	258	7	of	of	ADP
fcis-22002	258	8	service	service	NOUN
fcis-22002	258	9	systems	system	NOUN
fcis-22002	258	10	with	with	ADP
fcis-22002	258	11	priority	priority	NOUN
fcis-22002	258	12	reservation	reservation	NOUN
fcis-22002	258	13	,	,	PUNCT
fcis-22002	258	14	”	"	PUNCT
fcis-22002	258	15	in	in	ADP
fcis-22002	258	16	conf	conf	NOUN
fcis-22002	258	17	.	.	PUNCT
fcis-22002	259	1	rec	rec	PROPN
fcis-22002	259	2	.	.	PROPN
fcis-22002	259	3	1995	1995	NUM
fcis-22002	259	4	ieee	ieee	PROPN
fcis-22002	259	5	int	int	NOUN
fcis-22002	259	6	.	.	PUNCT
fcis-22002	259	7	conf	conf	PROPN
fcis-22002	259	8	.	.	PUNCT
fcis-22002	260	1	communications	communication	NOUN
fcis-22002	260	2	,	,	PUNCT
fcis-22002	260	3	pp	pp	ADJ
fcis-22002	260	4	.	.	PUNCT
fcis-22002	261	1	3–8	3–8	X
fcis-22002	261	2	.	.	PUNCT
fcis-22002	262	1	[	[	X
fcis-22002	262	2	8	8	NUM
fcis-22002	262	3	]	]	X
fcis-22002	262	4	w.	w.	PROPN
fcis-22002	262	5	shen	shen	PROPN
fcis-22002	262	6	,	,	PUNCT
fcis-22002	262	7	c.	c.	PROPN
fcis-22002	262	8	m.	m.	PROPN
fcis-22002	262	9	white	white	PROPN
fcis-22002	262	10	,	,	PUNCT
fcis-22002	262	11	and	and	CCONJ
fcis-22002	262	12	t.	t.	PROPN
fcis-22002	262	13	j.	j.	PROPN
fcis-22002	262	14	hazen	hazen	PROPN
fcis-22002	262	15	,	,	PUNCT
fcis-22002	262	16	“	"	PUNCT
fcis-22002	262	17	a	a	DET
fcis-22002	262	18	comparison	comparison	NOUN
fcis-22002	262	19	of	of	ADP
fcis-22002	262	20	query	query	NOUN
fcis-22002	262	21	by	by	ADP
fcis-22002	262	22	-	-	PUNCT
fcis-22002	262	23	example	example	NOUN
fcis-22002	262	24	methods	method	NOUN
fcis-22002	262	25	for	for	ADP
fcis-22002	262	26	spoken	spoken	ADJ
fcis-22002	262	27	term	term	NOUN
fcis-22002	262	28	detection	detection	NOUN
fcis-22002	262	29	,	,	PUNCT
fcis-22002	262	30	”	"	PUNCT
fcis-22002	262	31	massachusetts	massachusetts	PROPN
fcis-22002	262	32	inst	inst	NOUN
fcis-22002	262	33	of	of	ADP
fcis-22002	262	34	tech	tech	PROPN
fcis-22002	262	35	lexington	lexington	PROPN
fcis-22002	262	36	lincoln	lincoln	PROPN
fcis-22002	262	37	lab	lab	PROPN
fcis-22002	262	38	,	,	PUNCT
fcis-22002	262	39	tech	tech	NOUN
fcis-22002	262	40	.	.	PUNCT
fcis-22002	262	41	rep	rep	PROPN
fcis-22002	262	42	.	.	PROPN
fcis-22002	262	43	,	,	PUNCT
fcis-22002	262	44	2009	2009	NUM
fcis-22002	262	45	.	.	PUNCT
fcis-22002	263	1	g.	g.	PROPN
fcis-22002	263	2	w.	w.	PROPN
fcis-22002	263	3	juette	juette	PROPN
fcis-22002	263	4	and	and	CCONJ
fcis-22002	263	5	l.	l.	PROPN
fcis-22002	263	6	e.	e.	PROPN
fcis-22002	263	7	zeffanella	zeffanella	PROPN
fcis-22002	263	8	,	,	PUNCT
fcis-22002	263	9	“	"	PUNCT
fcis-22002	263	10	radio	radio	NOUN
fcis-22002	263	11	noise	noise	NOUN
fcis-22002	263	12	currents	current	NOUN
fcis-22002	263	13	n	n	DET
fcis-22002	263	14	short	short	ADJ
fcis-22002	263	15	sections	section	NOUN
fcis-22002	263	16	on	on	ADP
fcis-22002	263	17	bundle	bundle	NOUN
fcis-22002	263	18	conductors	conductor	NOUN
fcis-22002	263	19	(	(	PUNCT
fcis-22002	263	20	presented	present	VERB
fcis-22002	263	21	conference	conference	NOUN
fcis-22002	263	22	paper	paper	NOUN
fcis-22002	263	23	style	style	NOUN
fcis-22002	263	24	)	)	PUNCT
fcis-22002	263	25	,	,	PUNCT
fcis-22002	263	26	”	"	PUNCT
fcis-22002	263	27	presented	present	VERB
fcis-22002	263	28	at	at	ADP
fcis-22002	263	29	the	the	DET
fcis-22002	263	30	ieee	ieee	NOUN
fcis-22002	263	31	summer	summer	NOUN
fcis-22002	263	32	power	power	NOUN
fcis-22002	263	33	meeting	meeting	NOUN
fcis-22002	263	34	,	,	PUNCT
fcis-22002	263	35	dallas	dallas	PROPN
fcis-22002	263	36	,	,	PUNCT
fcis-22002	263	37	tx	tx	PROPN
fcis-22002	263	38	,	,	PUNCT
fcis-22002	263	39	jun	jun	PROPN
fcis-22002	263	40	.	.	PROPN
fcis-22002	263	41	22–27	22–27	NUM
fcis-22002	263	42	,	,	PUNCT
fcis-22002	263	43	1990	1990	NUM
fcis-22002	263	44	,	,	PUNCT
fcis-22002	263	45	paper	paper	NOUN
fcis-22002	263	46	90	90	NUM
fcis-22002	263	47	sm	sm	PROPN
fcis-22002	263	48	690	690	NUM
fcis-22002	263	49	-	-	SYM
fcis-22002	263	50	0	0	NUM
fcis-22002	263	51	pwrs	pwrs	NOUN
fcis-22002	263	52	.	.	PUNCT
fcis-22002	264	1	[	[	X
fcis-22002	264	2	9	9	NUM
fcis-22002	264	3	]	]	X
fcis-22002	264	4	h.	h.	PROPN
fcis-22002	264	5	sakoe	sakoe	PROPN
fcis-22002	264	6	and	and	CCONJ
fcis-22002	264	7	s.	s.	PROPN
fcis-22002	264	8	chiba	chiba	PROPN
fcis-22002	264	9	,	,	PUNCT
fcis-22002	264	10	“	"	PUNCT
fcis-22002	264	11	dynamic	dynamic	ADJ
fcis-22002	264	12	programming	programming	NOUN
fcis-22002	264	13	algorithm	algorithm	NOUN
fcis-22002	264	14	optimization	optimization	NOUN
fcis-22002	264	15	for	for	ADP
fcis-22002	264	16	spoken	speak	VERB
fcis-22002	264	17	word	word	NOUN
fcis-22002	264	18	recognition	recognition	NOUN
fcis-22002	264	19	,	,	PUNCT
fcis-22002	264	20	”	"	PUNCT
fcis-22002	264	21	ieee	ieee	NOUN
fcis-22002	264	22	transactions	transaction	NOUN
fcis-22002	264	23	on	on	ADP
fcis-22002	264	24	acoustics	acoustic	NOUN
fcis-22002	264	25	,	,	PUNCT
fcis-22002	264	26	speech	speech	NOUN
fcis-22002	264	27	,	,	PUNCT
fcis-22002	264	28	and	and	CCONJ
fcis-22002	264	29	signal	signal	NOUN
fcis-22002	264	30	processing	processing	NOUN
fcis-22002	264	31	,	,	PUNCT
fcis-22002	264	32	vol	vol	NOUN
fcis-22002	264	33	.	.	PROPN
fcis-22002	264	34	26	26	NUM
fcis-22002	264	35	,	,	PUNCT
fcis-22002	264	36	no	no	INTJ
fcis-22002	264	37	.	.	NOUN
fcis-22002	264	38	1	1	NUM
fcis-22002	264	39	,	,	PUNCT
fcis-22002	264	40	pp	pp	ADJ
fcis-22002	264	41	.	.	PUNCT
fcis-22002	265	1	43–49	43–49	NUM
fcis-22002	265	2	,	,	PUNCT
fcis-22002	265	3	1978	1978	NUM
fcis-22002	265	4	.	.	PUNCT
fcis-22002	266	1	j.	j.	PROPN
fcis-22002	266	2	williams	williams	PROPN
fcis-22002	266	3	,	,	PUNCT
fcis-22002	266	4	“	"	PUNCT
fcis-22002	266	5	narrow	narrow	ADJ
fcis-22002	266	6	-	-	PUNCT
fcis-22002	266	7	band	band	NOUN
fcis-22002	266	8	analyzer	analyzer	NOUN
fcis-22002	266	9	(	(	PUNCT
fcis-22002	266	10	thesis	thesis	NOUN
fcis-22002	266	11	or	or	CCONJ
fcis-22002	266	12	dissertation	dissertation	NOUN
fcis-22002	266	13	style	style	NOUN
fcis-22002	266	14	)	)	PUNCT
fcis-22002	266	15	,	,	PUNCT
fcis-22002	266	16	”	"	PUNCT
fcis-22002	266	17	ph.d	ph.d	PROPN
fcis-22002	266	18	.	.	PUNCT
fcis-22002	266	19	dissertation	dissertation	NOUN
fcis-22002	266	20	,	,	PUNCT
fcis-22002	266	21	dept	dept	NOUN
fcis-22002	266	22	.	.	PUNCT
fcis-22002	266	23	elect	elect	PROPN
fcis-22002	266	24	.	.	PUNCT
fcis-22002	267	1	eng	eng	PROPN
fcis-22002	267	2	.	.	PROPN
fcis-22002	267	3	,	,	PUNCT
fcis-22002	267	4	harvard	harvard	PROPN
fcis-22002	267	5	univ	univ	PROPN
fcis-22002	267	6	.	.	PROPN
fcis-22002	267	7	,	,	PUNCT
fcis-22002	267	8	cambridge	cambridge	PROPN
fcis-22002	267	9	,	,	PUNCT
fcis-22002	267	10	ma	ma	PROPN
fcis-22002	267	11	,	,	PUNCT
fcis-22002	267	12	1993	1993	NUM
fcis-22002	267	13	.	.	PUNCT
fcis-22002	268	1	[	[	X
fcis-22002	268	2	10	10	NUM
fcis-22002	268	3	]	]	PUNCT
fcis-22002	268	4	x.	x.	NOUN
fcis-22002	268	5	anguera	anguera	NOUN
fcis-22002	268	6	and	and	CCONJ
fcis-22002	268	7	m.	m.	NOUN
fcis-22002	268	8	ferrarons	ferraron	NOUN
fcis-22002	268	9	,	,	PUNCT
fcis-22002	268	10	“	"	PUNCT
fcis-22002	268	11	memory	memory	NOUN
fcis-22002	268	12	efficient	efficient	ADJ
fcis-22002	268	13	subsequence	subsequence	PROPN
fcis-22002	268	14	dtw	dtw	NOUN
fcis-22002	268	15	for	for	ADP
fcis-22002	268	16	query	query	NOUN
fcis-22002	268	17	-	-	PUNCT
fcis-22002	268	18	by	by	ADP
fcis-22002	268	19	-	-	PUNCT
fcis-22002	268	20	example	example	NOUN
fcis-22002	268	21	spoken	spoken	ADJ
fcis-22002	268	22	term	term	NOUN
fcis-22002	268	23	detection	detection	NOUN
fcis-22002	268	24	,	,	PUNCT
fcis-22002	268	25	”	"	PUNCT
fcis-22002	268	26	in	in	ADP
fcis-22002	268	27	2013	2013	NUM
fcis-22002	268	28	ieee	ieee	NOUN
fcis-22002	268	29	international	international	ADJ
fcis-22002	268	30	conference	conference	NOUN
fcis-22002	268	31	on	on	ADP
fcis-22002	268	32	multimedia	multimedia	NOUN
fcis-22002	268	33	and	and	CCONJ
fcis-22002	268	34	expo	expo	NOUN
fcis-22002	268	35	(	(	PUNCT
fcis-22002	268	36	icme	icme	PROPN
fcis-22002	268	37	)	)	PUNCT
fcis-22002	268	38	.	.	PUNCT
fcis-22002	269	1	ieee	ieee	PROPN
fcis-22002	269	2	,	,	PUNCT
fcis-22002	269	3	2013	2013	NUM
fcis-22002	269	4	,	,	PUNCT
fcis-22002	269	5	pp	pp	ADJ
fcis-22002	269	6	.	.	PUNCT
fcis-22002	270	1	1–6	1–6	X
fcis-22002	270	2	.	.	PUNCT
fcis-22002	271	1	j.	j.	PROPN
fcis-22002	271	2	p.	p.	PROPN
fcis-22002	271	3	wilkinson	wilkinson	PROPN
fcis-22002	271	4	,	,	PUNCT
fcis-22002	271	5	“	"	PUNCT
fcis-22002	271	6	nonlinear	nonlinear	ADJ
fcis-22002	271	7	resonant	resonant	ADJ
fcis-22002	271	8	circuit	circuit	NOUN
fcis-22002	271	9	devices	device	NOUN
fcis-22002	271	10	(	(	PUNCT
fcis-22002	271	11	patent	patent	NOUN
fcis-22002	271	12	style	style	NOUN
fcis-22002	271	13	)	)	PUNCT
fcis-22002	271	14	,	,	PUNCT
fcis-22002	271	15	”	"	PUNCT
fcis-22002	271	16	u.s	u.s	PROPN
fcis-22002	271	17	.	.	PROPN
fcis-22002	271	18	patent	patent	NOUN
fcis-22002	271	19	3	3	NUM
fcis-22002	271	20	624	624	NUM
fcis-22002	271	21	12	12	NUM
fcis-22002	271	22	,	,	PUNCT
fcis-22002	271	23	july	july	PROPN
fcis-22002	271	24	16	16	NUM
fcis-22002	271	25	,	,	PUNCT
fcis-22002	271	26	1990	1990	NUM
fcis-22002	271	27	.	.	PUNCT
fcis-22002	272	1	[	[	X
fcis-22002	272	2	11	11	NUM
fcis-22002	272	3	]	]	X
fcis-22002	272	4	h.	h.	PROPN
fcis-22002	272	5	kamper	kamper	PROPN
fcis-22002	272	6	,	,	PUNCT
fcis-22002	272	7	w.	w.	PROPN
fcis-22002	272	8	wang	wang	PROPN
fcis-22002	272	9	,	,	PUNCT
fcis-22002	272	10	and	and	CCONJ
fcis-22002	272	11	k.	k.	PROPN
fcis-22002	272	12	livescu	livescu	PROPN
fcis-22002	272	13	,	,	PUNCT
fcis-22002	272	14	“	"	PUNCT
fcis-22002	272	15	deep	deep	ADJ
fcis-22002	272	16	convolutional	convolutional	ADJ
fcis-22002	272	17	acous	acous	ADJ
fcis-22002	272	18	tic	tic	ADJ
fcis-22002	272	19	word	word	NOUN
fcis-22002	272	20	embeddings	embedding	NOUN
fcis-22002	272	21	using	use	VERB
fcis-22002	272	22	word	word	NOUN
fcis-22002	272	23	-	-	PUNCT
fcis-22002	272	24	pair	pair	NOUN
fcis-22002	272	25	side	side	NOUN
fcis-22002	272	26	information	information	NOUN
fcis-22002	272	27	,	,	PUNCT
fcis-22002	272	28	”	"	PUNCT
fcis-22002	272	29	in	in	ADP
fcis-22002	272	30	2016	2016	NUM
fcis-22002	272	31	ieee	ieee	NOUN
fcis-22002	272	32	international	international	ADJ
fcis-22002	272	33	conference	conference	NOUN
fcis-22002	272	34	on	on	ADP
fcis-22002	272	35	acoustics	acoustic	NOUN
fcis-22002	272	36	,	,	PUNCT
fcis-22002	272	37	speech	speech	NOUN
fcis-22002	272	38	and	and	CCONJ
fcis-22002	272	39	signal	signal	NOUN
fcis-22002	272	40	processing	processing	NOUN
fcis-22002	272	41	(	(	PUNCT
fcis-22002	272	42	icassp	icassp	PROPN
fcis-22002	272	43	)	)	PUNCT
fcis-22002	272	44	.	.	PUNCT
fcis-22002	273	1	ieee	ieee	PROPN
fcis-22002	273	2	,	,	PUNCT
fcis-22002	273	3	2016	2016	NUM
fcis-22002	273	4	,	,	PUNCT
fcis-22002	273	5	pp	pp	ADJ
fcis-22002	273	6	.	.	PUNCT
fcis-22002	274	1	4950–4954	4950–4954	NUM
fcis-22002	274	2	letter	letter	NOUN
fcis-22002	274	3	symbols	symbol	NOUN
fcis-22002	274	4	for	for	ADP
fcis-22002	274	5	quantities	quantity	NOUN
fcis-22002	274	6	,	,	PUNCT
fcis-22002	274	7	ansi	ansi	PROPN
fcis-22002	274	8	standard	standard	PROPN
fcis-22002	274	9	y10.5	y10.5	PROPN
fcis-22002	274	10	-	-	PROPN
fcis-22002	274	11	1968	1968	NUM
fcis-22002	274	12	.	.	PUNCT
fcis-22002	275	1	[	[	X
fcis-22002	275	2	12	12	NUM
fcis-22002	275	3	]	]	X
fcis-22002	275	4	c.	c.	PROPN
fcis-22002	275	5	jacobs	jacobs	PROPN
fcis-22002	275	6	,	,	PUNCT
fcis-22002	275	7	y.	y.	PROPN
fcis-22002	275	8	matusevych	matusevych	PROPN
fcis-22002	275	9	,	,	PUNCT
fcis-22002	275	10	and	and	CCONJ
fcis-22002	275	11	h.	h.	PROPN
fcis-22002	275	12	kamper	kamper	PROPN
fcis-22002	275	13	,	,	PUNCT
fcis-22002	275	14	“	"	PUNCT
fcis-22002	275	15	acoustic	acoustic	ADJ
fcis-22002	275	16	word	word	NOUN
fcis-22002	275	17	embeddings	embedding	NOUN
fcis-22002	275	18	for	for	ADP
fcis-22002	275	19	zero	zero	NUM
fcis-22002	275	20	-	-	PUNCT
fcis-22002	275	21	resource	resource	NOUN
fcis-22002	275	22	languages	language	NOUN
fcis-22002	275	23	using	use	VERB
fcis-22002	275	24	self	self	NOUN
fcis-22002	275	25	-	-	PUNCT
fcis-22002	275	26	supervised	supervise	VERB
fcis-22002	275	27	contrastive	contrastive	ADJ
fcis-22002	275	28	learning	learning	NOUN
fcis-22002	275	29	and	and	CCONJ
fcis-22002	275	30	multilingual	multilingual	ADJ
fcis-22002	275	31	adaptation	adaptation	NOUN
fcis-22002	275	32	,	,	PUNCT
fcis-22002	275	33	”	"	PUNCT
fcis-22002	275	34	in	in	ADP
fcis-22002	275	35	2021	2021	NUM
fcis-22002	275	36	ieee	ieee	NOUN
fcis-22002	275	37	spoken	speak	VERB
fcis-22002	275	38	language	language	NOUN
fcis-22002	275	39	technology	technology	NOUN
fcis-22002	275	40	workshop	workshop	NOUN
fcis-22002	275	41	(	(	PUNCT
fcis-22002	275	42	slt	slt	PROPN
fcis-22002	275	43	)	)	PUNCT
fcis-22002	275	44	.	.	PUNCT
fcis-22002	276	1	ieee	ieee	PROPN
fcis-22002	276	2	,	,	PUNCT
fcis-22002	276	3	2021	2021	NUM
fcis-22002	276	4	,	,	PUNCT
fcis-22002	276	5	pp	pp	ADJ
fcis-22002	276	6	.	.	PUNCT
fcis-22002	277	1	919–926	919–926	NUM
fcis-22002	277	2	.	.	PUNCT
fcis-22002	278	1	e.	e.	PROPN
fcis-22002	278	2	e.	e.	PROPN
fcis-22002	278	3	reber	reber	PROPN
fcis-22002	278	4	,	,	PUNCT
fcis-22002	278	5	r.	r.	PROPN
fcis-22002	278	6	l.	l.	PROPN
fcis-22002	278	7	michell	michell	PROPN
fcis-22002	278	8	,	,	PUNCT
fcis-22002	278	9	and	and	CCONJ
fcis-22002	278	10	c.	c.	PROPN
fcis-22002	278	11	j.	j.	PROPN
fcis-22002	278	12	carter	carter	PROPN
fcis-22002	278	13	,	,	PUNCT
fcis-22002	278	14	“	"	PUNCT
fcis-22002	278	15	oxygen	oxygen	NOUN
fcis-22002	278	16	absorption	absorption	NOUN
fcis-22002	278	17	in	in	ADP
fcis-22002	278	18	the	the	DET
fcis-22002	278	19	earth	earth	NOUN
fcis-22002	278	20	’s	’s	PART
fcis-22002	278	21	atmosphere	atmosphere	NOUN
fcis-22002	278	22	,	,	PUNCT
fcis-22002	278	23	”	"	PUNCT
fcis-22002	278	24	aerospace	aerospace	PROPN
fcis-22002	278	25	corp	corp	PROPN
fcis-22002	278	26	.	.	PROPN
fcis-22002	278	27	,	,	PUNCT
fcis-22002	278	28	los	los	PROPN
fcis-22002	278	29	angeles	angeles	PROPN
fcis-22002	278	30	,	,	PUNCT
fcis-22002	278	31	ca	can	AUX
fcis-22002	278	32	,	,	PUNCT
fcis-22002	278	33	tech	tech	NOUN
fcis-22002	278	34	.	.	PUNCT
fcis-22002	279	1	rep	rep	PROPN
fcis-22002	279	2	.	.	PROPN
fcis-22002	279	3	tr-0200	tr-0200	PROPN
fcis-22002	279	4	(	(	PUNCT
fcis-22002	279	5	420	420	NUM
fcis-22002	279	6	-	-	PUNCT
fcis-22002	279	7	46)-3	46)-3	NUM
fcis-22002	279	8	,	,	PUNCT
fcis-22002	279	9	nov	nov	PROPN
fcis-22002	279	10	.	.	PROPN
fcis-22002	279	11	1988	1988	NUM
fcis-22002	279	12	.	.	PUNCT
fcis-22002	280	1	[	[	X
fcis-22002	280	2	13	13	NUM
fcis-22002	280	3	]	]	X
fcis-22002	280	4	h.	h.	PROPN
fcis-22002	280	5	kamper	kamper	PROPN
fcis-22002	280	6	,	,	PUNCT
fcis-22002	280	7	y.	y.	PROPN
fcis-22002	280	8	matusevych	matusevych	PROPN
fcis-22002	280	9	,	,	PUNCT
fcis-22002	280	10	and	and	CCONJ
fcis-22002	280	11	s.	s.	PROPN
fcis-22002	280	12	goldwater	goldwater	PROPN
fcis-22002	280	13	,	,	PUNCT
fcis-22002	280	14	“	"	PUNCT
fcis-22002	280	15	improved	improve	VERB
fcis-22002	280	16	acoustic	acoustic	ADJ
fcis-22002	280	17	word	word	NOUN
fcis-22002	280	18	embeddings	embedding	NOUN
fcis-22002	280	19	for	for	ADP
fcis-22002	280	20	zero	zero	NUM
fcis-22002	280	21	-	-	PUNCT
fcis-22002	280	22	resource	resource	NOUN
fcis-22002	280	23	languages	language	NOUN
fcis-22002	280	24	using	use	VERB
fcis-22002	280	25	multilingual	multilingual	ADJ
fcis-22002	280	26	transfer	transfer	NOUN
fcis-22002	280	27	,	,	PUNCT
fcis-22002	280	28	”	"	PUNCT
fcis-22002	280	29	ieee	ieee	NOUN
fcis-22002	280	30	/	/	SYM
fcis-22002	280	31	acm	acm	NOUN
fcis-22002	280	32	transactions	transaction	NOUN
fcis-22002	280	33	on	on	ADP
fcis-22002	280	34	audio	audio	NOUN
fcis-22002	280	35	,	,	PUNCT
fcis-22002	280	36	speech	speech	NOUN
fcis-22002	280	37	,	,	PUNCT
fcis-22002	280	38	and	and	CCONJ
fcis-22002	280	39	language	language	NOUN
fcis-22002	280	40	processing	processing	NOUN
fcis-22002	280	41	,	,	PUNCT
fcis-22002	280	42	vol	vol	NOUN
fcis-22002	280	43	.	.	PROPN
fcis-22002	280	44	29	29	NUM
fcis-22002	280	45	,	,	PUNCT
fcis-22002	280	46	pp	pp	ADJ
fcis-22002	280	47	.	.	PUNCT
fcis-22002	281	1	1107–1118	1107–1118	NUM
fcis-22002	281	2	,	,	PUNCT
fcis-22002	281	3	2021	2021	NUM
fcis-22002	281	4	.	.	PUNCT
fcis-22002	282	1	[	[	X
fcis-22002	282	2	14	14	NUM
fcis-22002	282	3	]	]	PUNCT
fcis-22002	282	4	q.-y	q.-y	NOUN
fcis-22002	282	5	.	.	PUNCT
fcis-22002	283	1	zhang	zhang	PROPN
fcis-22002	283	2	,	,	PUNCT
fcis-22002	283	3	x.-j	x.-j	PROPN
fcis-22002	283	4	.	.	PUNCT
fcis-22002	284	1	zhao	zhao	PROPN
fcis-22002	284	2	,	,	PUNCT
fcis-22002	284	3	q.-w	q.-w	PROPN
fcis-22002	284	4	.	.	PUNCT
fcis-22002	285	1	zhang	zhang	PROPN
fcis-22002	285	2	,	,	PUNCT
fcis-22002	285	3	and	and	CCONJ
fcis-22002	285	4	y.-z	y.-z	PROPN
fcis-22002	285	5	.	.	PUNCT
fcis-22002	286	1	li	li	PROPN
fcis-22002	286	2	,	,	PUNCT
fcis-22002	286	3	“	"	PUNCT
fcis-22002	286	4	contentbased	contentbase	VERB
fcis-22002	286	5	encrypted	encrypt	VERB
fcis-22002	286	6	speech	speech	NOUN
fcis-22002	286	7	retrieval	retrieval	NOUN
fcis-22002	286	8	scheme	scheme	NOUN
fcis-22002	286	9	with	with	ADP
fcis-22002	286	10	deep	deep	ADJ
fcis-22002	286	11	hashing	hashing	NOUN
fcis-22002	286	12	,	,	PUNCT
fcis-22002	286	13	”	"	PUNCT
fcis-22002	286	14	multimedia	multimedia	NOUN
fcis-22002	286	15	tools	tool	NOUN
fcis-22002	286	16	and	and	CCONJ
fcis-22002	286	17	applications	application	NOUN
fcis-22002	286	18	,	,	PUNCT
fcis-22002	286	19	p.	p.	NOUN
fcis-22002	286	20	10221–10242	10221–10242	NUM
fcis-22002	286	21	,	,	PUNCT
fcis-22002	286	22	mar	mar	PROPN
fcis-22002	286	23	2022	2022	NUM
fcis-22002	286	24	.	.	PUNCT
fcis-22002	287	1	[	[	X
fcis-22002	287	2	15	15	NUM
fcis-22002	287	3	]	]	X
fcis-22002	287	4	y.	y.	PROPN
fcis-22002	287	5	yuan	yuan	PROPN
fcis-22002	287	6	,	,	PUNCT
fcis-22002	287	7	l.	l.	PROPN
fcis-22002	287	8	xie	xie	PROPN
fcis-22002	287	9	,	,	PUNCT
fcis-22002	287	10	c.-c	c.-c	PROPN
fcis-22002	287	11	.	.	PUNCT
fcis-22002	288	1	leung	leung	PROPN
fcis-22002	288	2	,	,	PUNCT
fcis-22002	288	3	h.	h.	PROPN
fcis-22002	288	4	chen	chen	PROPN
fcis-22002	288	5	,	,	PUNCT
fcis-22002	288	6	and	and	CCONJ
fcis-22002	288	7	b.	b.	PROPN
fcis-22002	288	8	ma	ma	PROPN
fcis-22002	288	9	,	,	PUNCT
fcis-22002	288	10	“	"	PUNCT
fcis-22002	288	11	fast	fast	ADJ
fcis-22002	288	12	query	query	NOUN
fcis-22002	288	13	-	-	PUNCT
fcis-22002	288	14	by	by	ADP
fcis-22002	288	15	example	example	NOUN
fcis-22002	288	16	speech	speech	NOUN
fcis-22002	288	17	search	search	NOUN
fcis-22002	288	18	using	use	VERB
fcis-22002	288	19	attention	attention	NOUN
fcis-22002	288	20	-	-	PUNCT
fcis-22002	288	21	based	base	VERB
fcis-22002	288	22	deep	deep	ADJ
fcis-22002	288	23	binary	binary	ADJ
fcis-22002	288	24	embeddings	embedding	NOUN
fcis-22002	288	25	,	,	PUNCT
fcis-22002	288	26	”	"	PUNCT
fcis-22002	288	27	ieee	ieee	NOUN
fcis-22002	288	28	/	/	SYM
fcis-22002	288	29	acm	acm	NOUN
fcis-22002	288	30	transactions	transaction	NOUN
fcis-22002	288	31	on	on	ADP
fcis-22002	288	32	audio	audio	NOUN
fcis-22002	288	33	,	,	PUNCT
fcis-22002	288	34	speech	speech	NOUN
fcis-22002	288	35	,	,	PUNCT
fcis-22002	288	36	and	and	CCONJ
fcis-22002	288	37	language	language	NOUN
fcis-22002	288	38	processing	processing	NOUN
fcis-22002	288	39	,	,	PUNCT
fcis-22002	288	40	vol	vol	NOUN
fcis-22002	288	41	.	.	PROPN
fcis-22002	288	42	28	28	NUM
fcis-22002	288	43	,	,	PUNCT
fcis-22002	288	44	pp	pp	ADJ
fcis-22002	288	45	.	.	PUNCT
fcis-22002	288	46	1988–2000	1988–2000	NUM
fcis-22002	288	47	,	,	PUNCT
fcis-22002	288	48	2020	2020	NUM
fcis-22002	288	49	.	.	PUNCT
fcis-22002	289	1	[	[	X
fcis-22002	289	2	16	16	NUM
fcis-22002	289	3	]	]	X
fcis-22002	289	4	s.-w	s.-w	PROPN
fcis-22002	289	5	.	.	PUNCT
fcis-22002	290	1	fan	fan	PROPN
fcis-22002	290	2	-	-	PUNCT
fcis-22002	290	3	jiang	jiang	PROPN
fcis-22002	290	4	,	,	PUNCT
fcis-22002	290	5	t.-h	t.-h	NOUN
fcis-22002	290	6	.	.	PUNCT
fcis-22002	291	1	lo	lo	PROPN
fcis-22002	291	2	,	,	PUNCT
fcis-22002	291	3	and	and	CCONJ
fcis-22002	291	4	b.	b.	PROPN
fcis-22002	291	5	chen	chen	PROPN
fcis-22002	291	6	,	,	PUNCT
fcis-22002	291	7	“	"	PUNCT
fcis-22002	291	8	spoken	speak	VERB
fcis-22002	291	9	document	document	NOUN
fcis-22002	291	10	retrieval	retrieval	NOUN
fcis-22002	291	11	leveraging	leverage	VERB
fcis-22002	291	12	bert	bert	NOUN
fcis-22002	291	13	-	-	PUNCT
fcis-22002	291	14	based	base	VERB
fcis-22002	291	15	modeling	modeling	NOUN
fcis-22002	291	16	and	and	CCONJ
fcis-22002	291	17	query	query	NOUN
fcis-22002	291	18	reformulation	reformulation	NOUN
fcis-22002	291	19	,	,	PUNCT
fcis-22002	291	20	”	"	PUNCT
fcis-22002	291	21	in	in	ADP
fcis-22002	291	22	icassp	icassp	PROPN
fcis-22002	291	23	2020	2020	NUM
fcis-22002	291	24	-	-	SYM
fcis-22002	291	25	2020	2020	NUM
fcis-22002	291	26	ieee	ieee	NOUN
fcis-22002	291	27	international	international	ADJ
fcis-22002	291	28	conference	conference	NOUN
fcis-22002	291	29	on	on	ADP
fcis-22002	291	30	acoustics	acoustic	NOUN
fcis-22002	291	31	,	,	PUNCT
fcis-22002	291	32	speech	speech	NOUN
fcis-22002	291	33	and	and	CCONJ
fcis-22002	291	34	sig	sig	NOUN
fcis-22002	291	35	nal	nal	ADJ
fcis-22002	291	36	processing	processing	NOUN
fcis-22002	291	37	(	(	PUNCT
fcis-22002	291	38	icassp	icassp	PROPN
fcis-22002	291	39	)	)	PUNCT
fcis-22002	291	40	.	.	PUNCT
fcis-22002	292	1	ieee	ieee	PROPN
fcis-22002	292	2	,	,	PUNCT
fcis-22002	292	3	2020	2020	NUM
fcis-22002	292	4	,	,	PUNCT
fcis-22002	292	5	pp	pp	ADJ
fcis-22002	292	6	.	.	PUNCT
fcis-22002	292	7	8144–8148	8144–8148	NUM
fcis-22002	292	8	.	.	PUNCT
fcis-22002	293	1	[	[	X
fcis-22002	293	2	17	17	NUM
fcis-22002	293	3	]	]	X
fcis-22002	293	4	h.	h.	PROPN
fcis-22002	293	5	muaidi	muaidi	PROPN
fcis-22002	293	6	,	,	PUNCT
fcis-22002	293	7	a.	a.	PROPN
fcis-22002	293	8	al	al	PROPN
fcis-22002	293	9	-	-	PUNCT
fcis-22002	293	10	ahmad	ahmad	PROPN
fcis-22002	293	11	,	,	PUNCT
fcis-22002	293	12	t.	t.	PROPN
fcis-22002	293	13	khdoor	khdoor	PROPN
fcis-22002	293	14	,	,	PUNCT
fcis-22002	293	15	s.	s.	PROPN
fcis-22002	293	16	alqrainy	alqrainy	PROPN
fcis-22002	293	17	,	,	PUNCT
fcis-22002	293	18	and	and	CCONJ
fcis-22002	293	19	m.	m.	PROPN
fcis-22002	293	20	alkof	alkof	PROPN
fcis-22002	293	21	fash	fash	PROPN
fcis-22002	293	22	,	,	PUNCT
fcis-22002	293	23	“	"	PUNCT
fcis-22002	293	24	arabic	arabic	ADJ
fcis-22002	293	25	audio	audio	NOUN
fcis-22002	293	26	news	news	NOUN
fcis-22002	293	27	retrieval	retrieval	NOUN
fcis-22002	293	28	system	system	NOUN
fcis-22002	293	29	using	use	VERB
fcis-22002	293	30	dependent	dependent	ADJ
fcis-22002	293	31	speaker	speaker	NOUN
fcis-22002	293	32	mode	mode	NOUN
fcis-22002	293	33	,	,	PUNCT
fcis-22002	293	34	mel	mel	PROPN
fcis-22002	293	35	frequency	frequency	PROPN
fcis-22002	293	36	cepstral	cepstral	ADJ
fcis-22002	293	37	coefficient	coefficient	NOUN
fcis-22002	293	38	and	and	CCONJ
fcis-22002	293	39	dynamic	dynamic	ADJ
fcis-22002	293	40	time	time	NOUN
fcis-22002	293	41	warp	warp	ADJ
fcis-22002	293	42	ing	ing	ADJ
fcis-22002	293	43	techniques	technique	NOUN
fcis-22002	293	44	,	,	PUNCT
fcis-22002	293	45	”	"	PUNCT
fcis-22002	293	46	research	research	NOUN
fcis-22002	293	47	journal	journal	NOUN
fcis-22002	293	48	of	of	ADP
fcis-22002	293	49	applied	apply	VERB
fcis-22002	293	50	sciences	science	NOUN
fcis-22002	293	51	,	,	PUNCT
fcis-22002	293	52	engineer	engineer	NOUN
fcis-22002	293	53	ing	ing	NOUN
fcis-22002	293	54	and	and	CCONJ
fcis-22002	293	55	technology	technology	NOUN
fcis-22002	293	56	,	,	PUNCT
fcis-22002	293	57	p.	p.	NOUN
fcis-22002	293	58	5082–5097	5082–5097	NUM
fcis-22002	293	59	,	,	PUNCT
fcis-22002	293	60	oct	oct	PROPN
fcis-22002	293	61	2016	2016	NUM
fcis-22002	293	62	.	.	PUNCT
fcis-22002	294	1	[	[	X
fcis-22002	294	2	online	online	X
fcis-22002	294	3	]	]	X
fcis-22002	294	4	.	.	PUNCT
fcis-22002	295	1	available	available	ADJ
fcis-22002	295	2	:	:	PUNCT
fcis-22002	295	3	http://dx.doi.org/	http://dx.doi.org/	PROPN
fcis-22002	295	4	10.19026/	10.19026/	NUM
fcis-22002	295	5	rjaset	rjaset	NOUN
fcis-22002	295	6	.	.	PUNCT
fcis-22002	296	1	7.903	7.903	NUM
fcis-22002	296	2	.	.	PUNCT
fcis-22002	297	1	[	[	X
fcis-22002	297	2	18	18	NUM
fcis-22002	297	3	]	]	X
fcis-22002	297	4	f.	f.	PROPN
fcis-22002	297	5	shen	shen	PROPN
fcis-22002	297	6	,	,	PUNCT
fcis-22002	297	7	c.	c.	PROPN
fcis-22002	297	8	du	du	PROPN
fcis-22002	297	9	,	,	PUNCT
fcis-22002	297	10	and	and	CCONJ
fcis-22002	297	11	k.	k.	PROPN
fcis-22002	297	12	yu	yu	PROPN
fcis-22002	297	13	,	,	PUNCT
fcis-22002	297	14	“	"	PUNCT
fcis-22002	297	15	acoustic	acoustic	ADJ
fcis-22002	297	16	word	word	NOUN
fcis-22002	297	17	embeddings	embedding	NOUN
fcis-22002	297	18	for	for	ADP
fcis-22002	297	19	end	end	NOUN
fcis-22002	297	20	-	-	PUNCT
fcis-22002	297	21	to	to	PART
fcis-22002	297	22	end	end	VERB
fcis-22002	297	23	speech	speech	NOUN
fcis-22002	297	24	synthesis	synthesis	NOUN
fcis-22002	297	25	,	,	PUNCT
fcis-22002	297	26	”	"	PUNCT
fcis-22002	297	27	applied	apply	VERB
fcis-22002	297	28	sciences	science	NOUN
fcis-22002	297	29	,	,	PUNCT
fcis-22002	297	30	p.	p.	NOUN
fcis-22002	297	31	9010	9010	NUM
fcis-22002	297	32	,	,	PUNCT
fcis-22002	297	33	sep	sep	NOUN
fcis-22002	297	34	2021	2021	NUM
fcis-22002	298	1	[	[	X
fcis-22002	298	2	online	online	X
fcis-22002	298	3	]	]	X
fcis-22002	298	4	.	.	PUNCT
fcis-22002	299	1	available	available	ADJ
fcis-22002	299	2	:	:	PUNCT
fcis-22002	299	3	http://	http://	PROPN
fcis-22002	299	4	dx.doi.org/	dx.doi.org/	X
fcis-22002	299	5	10.3390/	10.3390/	NUM
fcis-22002	299	6	app	app	NOUN
fcis-22002	299	7	1119901	1119901	NUM
fcis-22002	299	8	.	.	PUNCT
fcis-22002	300	1	[	[	X
fcis-22002	300	2	19	19	NUM
fcis-22002	300	3	]	]	PUNCT
fcis-22002	300	4	a.	a.	NOUN
fcis-22002	300	5	oord	oord	PROPN
fcis-22002	300	6	,	,	PUNCT
fcis-22002	300	7	s.	s.	PROPN
fcis-22002	300	8	dieleman	dieleman	PROPN
fcis-22002	300	9	,	,	PUNCT
fcis-22002	300	10	h.	h.	PROPN
fcis-22002	300	11	zen	zen	PROPN
fcis-22002	300	12	,	,	PUNCT
fcis-22002	300	13	k.	k.	PROPN
fcis-22002	300	14	simonyan	simonyan	PROPN
fcis-22002	300	15	,	,	PUNCT
fcis-22002	300	16	o.	o.	PROPN
fcis-22002	300	17	vinyals	vinyal	NOUN
fcis-22002	300	18	,	,	PUNCT
fcis-22002	300	19	a.	a.	NOUN
fcis-22002	300	20	graves	grave	NOUN
fcis-22002	300	21	,	,	PUNCT
fcis-22002	300	22	n.	n.	PROPN
fcis-22002	300	23	kalchbrenner	kalchbrenner	NOUN
fcis-22002	300	24	,	,	PUNCT
fcis-22002	300	25	a.	a.	NOUN
fcis-22002	300	26	senior	senior	NOUN
fcis-22002	300	27	,	,	PUNCT
fcis-22002	300	28	and	and	CCONJ
fcis-22002	300	29	k.	k.	PROPN
fcis-22002	300	30	kavukcuoglu	kavukcuoglu	PROPN
fcis-22002	300	31	,	,	PUNCT
fcis-22002	300	32	“	"	PUNCT
fcis-22002	300	33	wavenet	wavenet	NOUN
fcis-22002	300	34	:	:	PUNCT
fcis-22002	300	35	a	a	DET
fcis-22002	300	36	gener	gener	NOUN
fcis-22002	300	37	ative	ative	ADJ
fcis-22002	300	38	model	model	NOUN
fcis-22002	300	39	for	for	ADP
fcis-22002	300	40	raw	raw	ADJ
fcis-22002	300	41	audio	audio	NOUN
fcis-22002	300	42	,	,	PUNCT
fcis-22002	300	43	”	"	PUNCT
fcis-22002	300	44	enusssw	enusssw	PROPN
fcis-22002	300	45	,	,	PUNCT
fcis-22002	300	46	ssw	ssw	PROPN
fcis-22002	300	47	,	,	PUNCT
fcis-22002	300	48	sep	sep	PROPN
fcis-22002	300	49	2016	2016	NUM
fcis-22002	300	50	.	.	PUNCT
fcis-22002	301	1	[	[	X
fcis-22002	301	2	20	20	NUM
fcis-22002	301	3	]	]	X
fcis-22002	301	4	c.	c.	PROPN
fcis-22002	301	5	lea	lea	PROPN
fcis-22002	301	6	,	,	PUNCT
fcis-22002	301	7	m.	m.	PROPN
fcis-22002	301	8	d.	d.	PROPN
fcis-22002	301	9	flynn	flynn	PROPN
fcis-22002	301	10	,	,	PUNCT
fcis-22002	301	11	r.	r.	PROPN
fcis-22002	301	12	vidal	vidal	PROPN
fcis-22002	301	13	,	,	PUNCT
fcis-22002	301	14	a.	a.	NOUN
fcis-22002	301	15	reiter	reiter	PROPN
fcis-22002	301	16	,	,	PUNCT
fcis-22002	301	17	and	and	CCONJ
fcis-22002	301	18	g.	g.	PROPN
fcis-22002	301	19	d.	d.	PROPN
fcis-22002	301	20	hager	hager	PROPN
fcis-22002	301	21	,	,	PUNCT
fcis-22002	301	22	“	"	PUNCT
fcis-22002	301	23	tempo	tempo	NOUN
fcis-22002	301	24	ral	ral	ADJ
fcis-22002	301	25	convolutional	convolutional	ADJ
fcis-22002	301	26	networks	network	NOUN
fcis-22002	301	27	for	for	ADP
fcis-22002	301	28	action	action	NOUN
fcis-22002	301	29	segmentation	segmentation	NOUN
fcis-22002	301	30	and	and	CCONJ
fcis-22002	301	31	detection	detection	NOUN
fcis-22002	301	32	,	,	PUNCT
fcis-22002	301	33	”	"	PUNCT
fcis-22002	301	34	in	in	ADP
fcis-22002	301	35	proceedings	proceeding	NOUN
fcis-22002	301	36	of	of	ADP
fcis-22002	301	37	the	the	DET
fcis-22002	301	38	ieee	ieee	NOUN
fcis-22002	301	39	conference	conference	NOUN
fcis-22002	301	40	on	on	ADP
fcis-22002	301	41	computer	computer	NOUN
fcis-22002	301	42	vision	vision	NOUN
fcis-22002	301	43	and	and	CCONJ
fcis-22002	301	44	pattern	pattern	NOUN
fcis-22002	301	45	recognition	recognition	NOUN
fcis-22002	301	46	,	,	PUNCT
fcis-22002	301	47	2017	2017	NUM
fcis-22002	301	48	,	,	PUNCT
fcis-22002	301	49	pp	pp	ADJ
fcis-22002	301	50	.	.	PUNCT
fcis-22002	302	1	156–165	156–165	NUM
fcis-22002	302	2	.	.	PUNCT
fcis-22002	303	1	[	[	X
fcis-22002	303	2	21	21	NUM
fcis-22002	303	3	]	]	X
fcis-22002	303	4	h.	h.	PROPN
fcis-22002	303	5	wang	wang	PROPN
fcis-22002	303	6	,	,	PUNCT
fcis-22002	303	7	f.	f.	PROPN
fcis-22002	303	8	gao	gao	PROPN
fcis-22002	303	9	,	,	PUNCT
fcis-22002	303	10	y.	y.	PROPN
fcis-22002	303	11	zhao	zhao	PROPN
fcis-22002	303	12	,	,	PUNCT
fcis-22002	303	13	l.	l.	PROPN
fcis-22002	303	14	yang	yang	PROPN
fcis-22002	303	15	,	,	PUNCT
fcis-22002	303	16	j.	j.	PROPN
fcis-22002	303	17	yue	yue	PROPN
fcis-22002	303	18	,	,	PUNCT
fcis-22002	303	19	and	and	CCONJ
fcis-22002	303	20	h.	h.	PROPN
fcis-22002	303	21	ma	ma	PROPN
fcis-22002	303	22	,	,	PUNCT
fcis-22002	303	23	“	"	PUNCT
fcis-22002	303	24	multitask	multitask	ADJ
fcis-22002	303	25	learning	learning	NOUN
fcis-22002	303	26	with	with	ADP
fcis-22002	303	27	local	local	ADJ
fcis-22002	303	28	attention	attention	NOUN
fcis-22002	303	29	for	for	ADP
fcis-22002	303	30	tibetan	tibetan	ADJ
fcis-22002	303	31	speech	speech	NOUN
fcis-22002	303	32	recognition	recognition	NOUN
fcis-22002	303	33	,	,	PUNCT
fcis-22002	303	34	”	"	PUNCT
fcis-22002	303	35	complex	complex	ADJ
fcis-22002	303	36	ity	ity	PROPN
fcis-22002	303	37	,	,	PUNCT
fcis-22002	303	38	vol	vol	NOUN
fcis-22002	303	39	.	.	PUNCT
fcis-22002	303	40	2020	2020	NUM
fcis-22002	303	41	,	,	PUNCT
fcis-22002	303	42	pp	pp	ADP
fcis-22002	303	43	.	.	PUNCT
fcis-22002	304	1	1–10	1–10	NOUN
fcis-22002	304	2	,	,	PUNCT
fcis-22002	304	3	2020	2020	NUM
fcis-22002	304	4	.	.	PUNCT
fcis-22002	305	1	[	[	X
fcis-22002	305	2	22	22	NUM
fcis-22002	305	3	]	]	PUNCT
fcis-22002	305	4	b.-h	b.-h	NOUN
fcis-22002	305	5	.	.	PUNCT
fcis-22002	306	1	sung	sung	PROPN
fcis-22002	306	2	and	and	CCONJ
fcis-22002	306	3	s.-c	s.-c	PROPN
fcis-22002	306	4	.	.	PUNCT
fcis-22002	307	1	wei	wei	PROPN
fcis-22002	307	2	,	,	PUNCT
fcis-22002	307	3	“	"	PUNCT
fcis-22002	307	4	becmer	becmer	NOUN
fcis-22002	307	5	:	:	PUNCT
fcis-22002	307	6	a	a	DET
fcis-22002	307	7	fusion	fusion	NOUN
fcis-22002	307	8	model	model	NOUN
fcis-22002	307	9	using	use	VERB
fcis-22002	307	10	bert	bert	PROPN
fcis-22002	307	11	and	and	CCONJ
fcis-22002	307	12	cnn	cnn	PROPN
fcis-22002	307	13	for	for	ADP
fcis-22002	307	14	music	music	NOUN
fcis-22002	307	15	emotion	emotion	NOUN
fcis-22002	307	16	recognition	recognition	NOUN
fcis-22002	307	17	,	,	PUNCT
fcis-22002	307	18	”	"	PUNCT
fcis-22002	307	19	in	in	ADP
fcis-22002	307	20	2021	2021	NUM
fcis-22002	307	21	ieee	ieee	NOUN
fcis-22002	307	22	22nd	22nd	NOUN
fcis-22002	307	23	international	international	ADJ
fcis-22002	307	24	con	con	NOUN
fcis-22002	307	25	ference	ference	NOUN
fcis-22002	307	26	on	on	ADP
fcis-22002	307	27	information	information	NOUN
fcis-22002	307	28	reuse	reuse	NOUN
fcis-22002	307	29	and	and	CCONJ
fcis-22002	307	30	integration	integration	NOUN
fcis-22002	307	31	for	for	ADP
fcis-22002	307	32	data	data	NOUN
fcis-22002	307	33	science	science	NOUN
fcis-22002	307	34	(	(	PUNCT
fcis-22002	307	35	iri	iri	PROPN
fcis-22002	307	36	)	)	PUNCT
fcis-22002	307	37	.	.	PUNCT
fcis-22002	308	1	ieee	ieee	NOUN
fcis-22002	308	2	,	,	PUNCT
fcis-22002	308	3	2021	2021	NUM
fcis-22002	308	4	,	,	PUNCT
fcis-22002	308	5	pp	pp	ADP
fcis-22002	308	6	.	.	PUNCT
fcis-22002	309	1	437–444	437–444	NUM
fcis-22002	309	2	.	.	PUNCT
fcis-22002	310	1	[	[	X
fcis-22002	310	2	23	23	NUM
fcis-22002	310	3	]	]	PUNCT
fcis-22002	310	4	j.	j.	PROPN
fcis-22002	310	5	mingyu	mingyu	PROPN
fcis-22002	310	6	,	,	PUNCT
fcis-22002	310	7	z.	z.	PROPN
fcis-22002	310	8	jiawei	jiawei	PROPN
fcis-22002	310	9	,	,	PUNCT
fcis-22002	310	10	and	and	CCONJ
fcis-22002	310	11	w.	w.	PROPN
fcis-22002	310	12	ning	ning	PROPN
fcis-22002	310	13	,	,	PUNCT
fcis-22002	310	14	“	"	PUNCT
fcis-22002	310	15	afr	afr	NOUN
fcis-22002	310	16	-	-	PUNCT
fcis-22002	310	17	bert	bert	PROPN
fcis-22002	310	18	:	:	PUNCT
fcis-22002	310	19	attention	attention	NOUN
fcis-22002	310	20	-	-	PUNCT
fcis-22002	310	21	based	base	VERB
fcis-22002	310	22	mech	mech	NOUN
fcis-22002	310	23	anism	anism	NOUN
fcis-22002	310	24	feature	feature	NOUN
fcis-22002	310	25	relevance	relevance	NOUN
fcis-22002	310	26	fusion	fusion	NOUN
fcis-22002	310	27	multimodal	multimodal	NOUN
fcis-22002	310	28	sentiment	sentiment	NOUN
fcis-22002	310	29	analysis	analysis	NOUN
fcis-22002	310	30	model	model	NOUN
fcis-22002	310	31	,	,	PUNCT
fcis-22002	310	32	”	"	PUNCT
fcis-22002	310	33	plos	plos	PROPN
fcis-22002	310	34	one	one	NUM
fcis-22002	310	35	,	,	PUNCT
fcis-22002	310	36	vol	vol	NOUN
fcis-22002	310	37	.	.	PROPN
fcis-22002	310	38	17	17	NUM
fcis-22002	310	39	,	,	PUNCT
fcis-22002	310	40	no	no	INTJ
fcis-22002	310	41	.	.	NOUN
fcis-22002	310	42	9	9	NUM
fcis-22002	310	43	,	,	PUNCT
fcis-22002	310	44	p.	p.	NOUN
fcis-22002	310	45	e0273936	e0273936	PROPN
fcis-22002	310	46	,	,	PUNCT
fcis-22002	310	47	2022	2022	NUM
fcis-22002	310	48	.	.	PUNCT
fcis-22002	311	1	[	[	X
fcis-22002	311	2	24	24	NUM
fcis-22002	311	3	]	]	X
fcis-22002	311	4	d.	d.	PROPN
fcis-22002	311	5	wang	wang	PROPN
fcis-22002	311	6	and	and	CCONJ
fcis-22002	311	7	x.	x.	PROPN
fcis-22002	311	8	zhang	zhang	PROPN
fcis-22002	311	9	,	,	PUNCT
fcis-22002	311	10	“	"	PUNCT
fcis-22002	311	11	thchs-30	thchs-30	PROPN
fcis-22002	311	12	:	:	PUNCT
fcis-22002	311	13	a	a	DET
fcis-22002	311	14	free	free	ADJ
fcis-22002	311	15	chinese	chinese	ADJ
fcis-22002	311	16	speech	speech	NOUN
fcis-22002	311	17	corpus	corpus	NOUN
fcis-22002	311	18	,	,	PUNCT
fcis-22002	311	19	”	"	PUNCT
fcis-22002	311	20	arxiv	arxiv	PROPN
fcis-22002	311	21	preprint	preprint	NOUN
fcis-22002	311	22	arxiv:1512.01882	arxiv:1512.01882	PROPN
fcis-22002	311	23	,	,	PUNCT
fcis-22002	311	24	2015	2015	NUM
fcis-22002	311	25	.	.	PUNCT