AP09_2.vp


1 Introduction
Automatic Speech Recognition (ASR) in a noisy envi-

ronment has been a challenging issue in recent decades for
many research centers, as the presence of noise significantly
decreases the accuracy of ASR systems. There are several ap-
proaches to compensate the effect of unclean conditions,
which can be combined together with more or less advanta-
geous results.

The first class of these methods is applied before acoustic
modelling in front-end signal preprocessing. The signal is
standardly represented by auditory-based features PLPs [1]
or MFCCs to minimize the effect of speaker variability. Then
noise suppression methods, such as most widely used Spec-
tral Subtraction (SS) [2], Wiener filtering, and Minimum
Mean Square Error (MMSE) estimation [3], are applied
within front-end signal processing to minimize the noise level
background in the analyzed speech.

The second class involves approaches, that take effect in
the modelling phase. The models of speech and pause are
typically trained on clean speech data to ensure high quality
of the final models of speech. Model adaptation transforms
clean speech models to perform well in a noisy environment.
Several adaptation techniques use background noise, which is
combined with the speech signal e.g. in multi-environment
models [4], or with acoustic models in parallel model combi-
nation (PMC) [5]. Other techniques use noisy speech data to
adapt acoustic models for particular background conditions
by simply retraining the clean speech models or by some
transformation using maximum likelihood linear regression
(MLLR) [6] or Maximum A Posteriori (MAP) adaptation [7].
The latter two schemes are also used also for speaker adapta-
tion with only a small proportion of adaptation material.

Due to varying or unknown target background conditions,
and due to the high costs of collecting speech data in a real
environment, not enough data that matches the recognition
conditions for the adaptation procedure is typically available.
Therefore a set of data for “almost matched” conditions is
often used for training or model adaptation [4, 8].

In [9], clean speech was mixed additively with real noise
from a car to get adaptation data. The final models were then
adapted on these recordings by MLLR and MAP, with a re-
sulting improvement from 14.38 % to 5.73 %. The authors
show the advantage of using noisy data for training speech
models in a car environment using additive mixing of clean
speech and noise. Similarly, an additive noise approach out-
performed the recognition results of a baseline system in
different environmental conditions trained and tested on the
Aurora 2 database in [10].

Unlike using only additive noise, data recorded in real
conditions is used in this paper. The aim of our work is to
analyse the influence of using multi-environment training
material for robust speech recognition in an unknown envi-
ronment. The recordings from the real world are important
from the point of view of the real influence of noisy condi-
tions. Not only additive distortion but also convolutional
distortion are taken into account.

As shown e.g. in [11], joint usage of spectral subtraction
and MLLR adaptation seems to be a good framework for
a recognition task under conditions with a high level of
background noise. These techniques can be used for blind
adaptation, and they are therefore also useful for unknown
noise reduction. This paper describes the effect of multi-
-condition training in several phases of the noise reduction
algorithm shown in Fig. 1.

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Acta Polytechnica Vol. 49  No. 2–3/2009

Multi-Condition Training for Unknown
Environment Adaptation in Robust ASR

Under Real Conditions
J. Rajnoha

Automatic speech recognition (ASR) systems frequently work in a noisy environment. As they are often trained on clean speech data, noise
reduction or adaptation techniques are applied to decrease the influence of background disturbance even in the case of unknown conditions.
Speech data mixed with noise recordings from particular environment are often used for the purposes of model adaptation. This paper anal-
yses the improvement of recognition performance within such adaptation when multi-condition training data from a real environment is
used for training initial models. Although the quality of such models can decrease with the presence of noise in the training material, they are
assumed to include initial information about noise and consequently support the adaptation procedure. Experimental results show signifi-
cant improvement of the proposed training method in a robust ASR task under unknown noisy conditions. The decrease by 29 % and 14 %
in word error rate in comparison with clean speech training data was achieved for the non-adapted and adapted system, respectively.

Keywords: speech recognition, environment adaptation, spectral subtraction, MLLR, noisy background.

clean

multi-condition

unknown
speech

ESS

singlepass

MLLR

baseline
HMM

decoder

retraining/adaptation

final
HMM

ESS

training material

Fig. 1: Block scheme of the noise reduction algorithm



2 Noise compensation methods
The spectral subtraction technique is standardly used in

front-end processing as a blind noise suppression method
(see Fig. 1). Model parameters can be subsequently changed
using single-pass retraining as a simple approach for offline
adaptation or MLLR as a standard method which can be used
for both offline and online adaptation.

2.1 Spectral subtraction
Spectral Subtraction (SS) is a technique frequently used

for the suppressing the additive background noise compo-
nent in the spectral domain to eliminate stationary or non-
-stationary noise with rather slow changes in characteristics.
The characteristics of noise are estimated from speech pauses
found e.g. by the Voice Activity Detector (VAD), which can of-
ten the limiting point of the algorithm. In our work,
extended Spectral Subtraction (ESS) [12] uses modified
adaptive Wiener filtering working without VAD.

2.2 Single-pass retraining
Single-pass retraining of the models is often used when a

large amount of data with matching environmental condi-
tions is available and offline retraining of acoustic models
can be performed. The parameters of clean speech models
are changed within one pass of the retraining procedure.
This retraining is performed on the set of recordings with a
matching environmental background. Such data will be called
matching data in the following text. The disadvantage of this
approach lies in the need for a sufficient amount of matching
data for each model. This can be very difficult, mainly in the
case of a specific environment. In addition a large number of
speakers are needed for speaker independent recognition
tasks. For these reasons, single-pass retraining was used as a
low-bound result for unsupervised speaker adaptation experi-
ments with an increasing amount of adaptation data in [6].

2.3 MLLR
As noted above, there is often not enough available data

for the single-pass retraining procedure. MLLR uses a small
amount of adaptation material to estimate an affine linear
transform A, b of the model parameters, which is found in
terms of minimizing the likelihood of adaptation data. Based
on our preliminary tests, we use only MLLR of mean vectors
for our experiments, and other model parameters are un-
changed. The new mean vector is then given by

� �new old� �A b . (1)

The same transform A, b can be applied to the mean vec-
tor of all models (global adaptation) or the models can be
clustered on the basis of acoustic similarity into several classes.
Separate transforms are then applied to particular classes.
This clustering can represent the different effect of back-
ground distortion on particular speech phones. The regres-
sion class approach also enables us to cluster the models
according to the amount of adaptation data to ensure suffi-
cient quality of the transform. Binary regression class tree
clustering [13] is used in this work.

3 Experiments
The experiments were performed on a small vocabulary

speaker independent (SI) speech recognition task. The Czech
digit sequence recogniser based on HMMs of monophones
was used for this purpose.

3.1 Front-end setup
Front-end signal processing was carried out using the

CtuCopy parametrization tool [14]. This enables similar func-
tionality to the HTK HCopy tool [13] and provides ad-
ditional noise reduction algorithms, e.g. VAD detection,
spectral subtraction and LDA RASTA-like filtration.

Table 1 summarizes the overall setting of the recognition
front-end.

3.2 Databases
The Czech Speecon database [15] was used for training

and testing, i.e. 16 kHz data recorded in different environ-
ments using several types of microphones. The database in-
volves utterances from almost 600 speakers with different
content, e.g. phonetically rich sentences, digits, commands,
etc.

Table 2 shows the division of the database in accordance
with various environmental conditions. The whole database
(ALL) was divided on the basis of type of recording environ-
ment (CLEAN and NOISY) or estimated SNR level (HISNR
and LOSNR). Subsets with specific environment (OFFICE and
CAR) were also created.

Each subset was divided into a training part and a testing
part, taking into account a sufficient number of speakers for

4 ©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/

Acta Polytechnica Vol. 49  No. 2–3/2009

segmentation window
25 ms

Hamming window

segmentation step 10 ms

feature extraction
1 energy, 12 MFCCs
+delta+acc. coeffs

models
HMMs of monophones

32 mixtures

Table 1: Front-end setup

Name Description SNR [dB]

CS0 CS1

ALL Whole SPEECON database 24.03 18.26

OFFICE Very clean office recordings 26.91 19.88

CAR Recordings in a car 13.33 8.43

CLEAN Clean environment subset 27.15 20.80

NOISY Noisy environment subset 21.25 15.44

HISNR High SNR subset 27.51 20.36

LOSNR Low SNR subset 13.76 12.07

Table 2: Description of SPEECON subsets and average estimated
SNR



SI recognition task. Training was performed on head-set mi-
crophone (CS0) data. Only the subsets ALL and OFFICE were
used for training to simulate multi-condition training or clean
data training, respectively.

Data from two different channels using a head-set micro-
phone (CS0) and hands-free set (CS1) was used for testing.
The CS1 channel is assumed to capture a higher level of back-
ground noise, which is illustrated in the estimated SNR values
in Table 2. Each testing subset was divided for retraining or
adaptation purposes according to the content, into a testing
subset, which involves digits only, and a subset with the rest of
the testing set, called the matched set.

As noted in sec. 2.3, the MLLR adaptation technique can
work with a low amount of adaptation data. Subsets con-
taining 20, 50, 100, 200, 500 and 1000 utterances were
therefore created from each matched subset for comparison
purposes. For the speaker-independent recognition task,
each such subset involved as many speakers as possible. Not
fewer than 18 speakers were present in the final subsets. This
number can be considered as sufficient with regard to the
number of speakers used in [9] (10–80) to get improvement
in a speaker-independent task. Table 3 shows the average
amount of adaptation data for different limits of utterances.

3.3 Spectral subtraction in different conditions
Training the models on clean data, the presence of envi-

ronmental distortion significantly decreases the recognition
accuracy. As Table 4 shows, using ESS helps to suppress the in-
fluence of unclean conditions. Although the results are worse

for matching conditions (Clean, CS0), the overall results give
more than 8 % of WER enhancement.

A similar improvement was achieved for multi-condition
training (Table 5). Although the unclean environment in the
training phase decreases the quality of the resulting models,
the overall contribution of using the multi-condition training
database with ESS against the case of clean training data
(Table 6) is almost 30 % of WER.

3.4 Single-pass retraining
All matching data for particular testing subsets was used

for single-pass retraining in the case of clean or multi-condi-
tion training data. With regard to the results in section 3.3,
ESS was used within front-end signal processing in the follow-
ing experiments.

Table 7 shows that using multi-condition training data for
training the initial models for single-pass retraining brings an
improvement of over 22% against the clean speech models.
All available matching data were used in this experiment,
which led to the final set of 2400 (CAR) – 11600 (ALL) utter-
ances for retraining.

3.5 MLLR
Single-pass retraining acts as a low-bound value for envi-

ronment adaptation, as the amount of data for retraining is
rather high. Only a limited amount of adaptation material
for particular conditions is available in a real system, and
decreasing the proportion of data for single-pass retraining
procedure can lead to a significant decrease in recognition ac-
curacy. MLLR-based adaptation removes this disadvantage.
As shown in Fig. 2, even for a low amount of adaptation data
the accuracy of a MLLR-adapted system outperforms the
baseline and single-pass results.

Section 3.2 describes the adaptation subsets with a limited
number of utterances, which reduces the computational load
of the adaptation procedure. The recognition tests were per-
formed on each subset and the results presented here show
the value averaged over all these limited adaptation subsets.

©  Czech Technical University Publishing House http://ctn.cvut.cz/ap/ 5

Acta Polytechnica Vol. 49  No. 2–3/2009

utter. 20 50 100 200 500 1000 all

time
65
s

2.8
min

5.7
min

10.9
min

26.9
min

57.2
min

7.8
h

Table 3: Average amount of speech data for limited adaptation
subsets

ALL OFFICE CAR CLEAN NOISY HISNR LOSNR AVG avg avg

CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1

noSS 8.32 14.72 3.47 8.14 7.03 35.17 4.0 11.07 13.09 18.79 7.03 10.2 11.88 17.65 12.18 7.83 16.53

SS 8.46 13.53 4.17 8.01 5.2 29.97 4.45 9.82 11.31 16.56 6.83 9.41 11.76 16.31 11.13 7.45 14.8

Imp. �1.68 8.08 �20.17 1.6 26.03 14.79 �11.25 11.29 13.6 11.87 2.84 7.75 1.01 7.59 8.66 4.82 10.48

Table 4: WER for different environmental conditions w/o and with ESS in front-end processing. The models are trained on clean data.

ALL OFFICE CAR CLEAN NOISY HISNR LOSNR AVG avg avg

CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1

noSS 7.77 10.65 3.87 9.61 3.36 8.26 4.79 11.19 10.6 10.95 6.68 10.0 10.37 12.81 8.64 6.78 10.5

SS 7.04 10.65 3.87 7.21 1.53 8.87 4.68 9.7 8.55 10.77 5.74 9.55 9.78 12.58 7.89 5.88 9.9

Imp. 9.4 0.0 0.0 24.97 54.46 �7.38 2.3 13.32 19.34 1.64 14.07 4.5 5.69 1.8 8.59 13.17 5.63

Table 5: WER for different environmental conditions w/o and with ESS in front-end processing. The models are trained on multi-condi-
tion data



Various settings of model clustering for regression tree-
-based adaptation according to section 2.3 were used within
the experiments. Global transformation and the division into
2, 4, 8, 16 and 32 regression classes were used, and the case
with minimum achieved WER is reported in the following
table.

The recognition results in Table 8 again show the im-
provement for using multi-condition training material for ini-
tial models. Only the case for very clean conditions (Clean,
CS0) brings a slight decrease in WER. The contribution is
evidentavg mainly for channel mismatch (CS1).

3.6 Overall improvement
Fig. 3 summarizes the contribution of using multi-condi-

tion training data for initial training in particular phases of
the noise reduction procedure. The use of multi-condition
training data leads to a significant improvement in all phases
of the system.

The proposed noise reduction method led to the en-
hancement of WER by 48 %. The improvement achieved by

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Acta Polytechnica Vol. 49  No. 2–3/2009

ALL OFFICE CAR CLEAN NOISY HISNR LOSNR AVG avg avg

CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1

Clean 8.46 13.53 4.17 8.01 5.2 29.97 4.45 9.82 11.31 16.56 6.83 9.41 11.76 16.31 11.13 7.45 14.8

M-C 7.04 10.65 3.87 7.21 1.53 8.87 4.68 9.7 8.55 10.77 5.74 9.55 9.78 12.58 7.89 5.88 9.9

Imp. 16.78 21.29 7.19 9.99 70.58 70.4 �5.17 1.22 24.4 34.96 15.96 �1.49 16.84 22.87 29.06 21.06 33.09

Table 6: WER for clean (Clean) and multi-condition (M-C) training data and relative improvement for multi-condition training against
clean training – no retraining/adaptation

ALL OFFICE CAR CLEAN NOISY HISNR LOSNR AVG avg avg

CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1

Clean 7.4 10.05 3.74 6.94 3.36 14.37 5.02 8.11 8.9 12.82 6.24 8.22 11.24 12.46 8.49 6.56 10.42

M-C 6.67 8.32 3.47 6.01 2.75 5.2 4.91 7.42 7.48 7.57 5.54 7.92 9.84 9.03 6.58 5.81 7.35

Imp. 9.86 17.21 7.22 13.4 18.15 63.81 2.19 8.51 15.96 40.95 11.22 3.65 12.46 27.53 22.5 11.42 29.46

Table 7: WER for clean (Clean) and multi-condition (M-C) training data and relative improvement for multi-condition training against
clean training – single-pass retraining

ALL OFFICE CAR CLEAN NOISY HISNR LOSNR AVG avg avg

CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1 CS0 CS1

Clean 7.82 8.78 3.81 5.7 2.86 5.05 4.45 7.1 10.18 10.75 6.84 7.24 10.66 10.52 7.43 6.81 8.01

M-C 7.18 7.44 3.54 5.36 2.4 3.01 4.53 6.68 8.46 7.7 5.99 7.2 9.53 8.83 6.38 6.1 6.66

Imp. 8.29 15.21 7.05 5.88 16.11 40.37 �1.68 5.89 16.88 28.31 12.42 0.58 10.56 16.03 14.06 10.4 16.84

Table 8: WER for clean (Clean) and multi-condition (M-C) training data and relative improvement for multi-condition training against
clean training – MLLR adaptation

0
10
20
30
40
50
60
70
80
90

100

all1k5002001005020

W
E

R
[%

]

No. of retraining utterances

baseline
singlepass

MLLR

Fig. 2: WER for different amount of training data for single-pass
retraining and MLLR adaptation

0

2

4

6

8

10

12

no SS SS singlepass MLLR

W
E

R
[%

]

system

Cleantraining
M-C training

Fig. 3: Average WER in particular phases of noise reduction for
clean and multi-condition training



multi-condition training brought a more than 14 % decrease
in recognition error.

4 Conclusion
The paper shows the advantages of using a multi-condi-

tion training data for robust ASR in unknown background
conditions. The main contribution of the work is in using
recordings from a real environment, which reflects the real
influence of noise in a robust recognition task.

The results can be summarized in the following points.
� Multi-condition (M-C) training brings significant improve-

ment to recognition accuracy, even in the case without any
other noise reduction method. In the results presented
here, multi-condition training outperforms the system that
uses spectral subtraction and clean training data by 22 %.
A combination of M-C training and spectral subtraction
algorithm resulted in more than 29 % enhancement of
WER against the baseline system. An increase in recogni-
tion accuracy by more than 70 % can be observed for data
recorded in a car.

� Single-pass retraining gives a robust offline procedure for
correcting acoustic models when enough matching data is
available for a high variety of speakers and a rich phonetic
content. The main contribution was observed for chan-
nel mismatch, and the use of M-C trained initial models
brought an additional improvement to these results.

� Advantageous clustering of models based on available ad-
aptation data within MLLR adaptation is shown to bring
an improvement over single-pass retraining. The final im-
provement using M-C trained models only slightly outper-
forms the single-pass results.

Generally, multi-condition training material for initial
training of speech models seems to bring an improvement to
the recognition task in unknown environmental conditions.
As the training and testing data in our experiments come
from the same source, future work will be oriented to higher
mismatches in adaptation and recognition conditions.

Acknowledgements
This research has been supported by grants GAČR

102/08/0707 “Speech Recognition under Real-World Con-
ditions”, GAČR 102/08/H008 “Analysis and modelling bio-
medical and speech signals”, and by research activity MSM
6840770014 “Perspective Informative and Communications
Technicalities Research”.

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Josef Rajnoha
e-mail: rajnoj1@fel.cvut.cz

Department of Circuit Theory

Czech Technical University in Prague
Faculty of Electrical Engineering
Technická 2
166 27 Praha, Czech Republic

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    /ENU (Use these settings to create Adobe PDF documents best suited for high-quality prepress printing.  Created PDF documents can be opened with Acrobat and Adobe Reader 5.0 and later.)
  >>
  /Namespace [
    (Adobe)
    (Common)
    (1.0)
  ]
  /OtherNamespaces [
    <<
      /AsReaderSpreads false
      /CropImagesToFrames true
      /ErrorControl /WarnAndContinue
      /FlattenerIgnoreSpreadOverrides false
      /IncludeGuidesGrids false
      /IncludeNonPrinting false
      /IncludeSlug false
      /Namespace [
        (Adobe)
        (InDesign)
        (4.0)
      ]
      /OmitPlacedBitmaps false
      /OmitPlacedEPS false
      /OmitPlacedPDF false
      /SimulateOverprint /Legacy
    >>
    <<
      /AddBleedMarks false
      /AddColorBars false
      /AddCropMarks false
      /AddPageInfo false
      /AddRegMarks false
      /ConvertColors /ConvertToCMYK
      /DestinationProfileName ()
      /DestinationProfileSelector /DocumentCMYK
      /Downsample16BitImages true
      /FlattenerPreset <<
        /PresetSelector /MediumResolution
      >>
      /FormElements false
      /GenerateStructure false
      /IncludeBookmarks false
      /IncludeHyperlinks false
      /IncludeInteractive false
      /IncludeLayers false
      /IncludeProfiles false
      /MultimediaHandling /UseObjectSettings
      /Namespace [
        (Adobe)
        (CreativeSuite)
        (2.0)
      ]
      /PDFXOutputIntentProfileSelector /DocumentCMYK
      /PreserveEditing true
      /UntaggedCMYKHandling /LeaveUntagged
      /UntaggedRGBHandling /UseDocumentProfile
      /UseDocumentBleed false
    >>
  ]
>> setdistillerparams
<<
  /HWResolution [2400 2400]
  /PageSize [612.000 792.000]
>> setpagedevice