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Title

Comparative evaluation of modulation‑transfer‑function‑based blind restoration of sub‑band
power envelopes of speech as a front‑end
processor for automatic speech recognition
systems

Author(s)Lu, Xugang; Unoki, Masashi; Akagi, Masato

Citation

Acoustical Science and Technology, 29(6): 351‑361

Issue Date

2008

Type

Journal Article

Text version

publisher

URL

http://hdl.handle.net/10119/7750

RightsCopyright (C)2008 日本音響学会, Xugang Lu,
Masashi Unoki and Masato Akagi, Acoustical
Science and Technology, 29(6), 2008, 351‑361.

Description

Japan Advanced Institute of Science and Technology#2008 The Acoustical Society of Japan

Acoust. Sci. & Tech. 29, 6 (2008)

PAPER

Comparative evaluation of modulation-transfer-function-based blind
restoration of sub-band power envelopes of speech
as a front-end processor for automatic speech recognition systems
Xugang Lu, Masashi Unokiy and Masato Akagiz
School of Information Science, Japan Advanced Institute of Science and Technology,
1–1 Asahidai, Nomi, 923–1292 Japan( Received 5 September 2007, Accepted for publication 2 May 2008 )Abstract: To reduce speech degradation in reverberant environments, we previously proposed a
modulation-transfer-function (MTF)-based method of speech dereverberation. By considering the
temporal modulation properties of speech, and the exponential decay properties of the power envelopeof the impulse response of room acoustics, we obtained the following MTF relation: the sub-band
power envelope of reverberant speech that can be represented as a convolution between the sub-band
power envelope of clean speech and the power envelope of the impulse response of room acoustics. On
the basis of the MTF relation, inverse MTF ﬁltering can be applied to restoring the power envelopes ofreverberant speech. Therefore, the impulse response of the room acoustics in this restoration dose notneed to be measured at any time since we model the power envelope of the impulse response as an
exponential decay function. We have tested how eﬀective this method is as a front-end for automatic
speech recognition (ASR) systems in artiﬁcial and real reverberant environments. Reverberant speech
signals were created by simply convoluting clean speech (AURORA-2J database) with the artiﬁcially
produced or real impulse responses of room acoustics. A method based on the auditory power
spectrum was used as a baseline for comparison. Compared with the baseline, the proposed method for
artiﬁcial reverberant environments produced a 35.67% relative improvement in the error reduction rate
(on average, for reverberation times from 0.2 to 2.0 s), and for real reverberant environments (43reverberant impulse responses), it produced a 25.78% relative improvement in the error reduction rate.
The results demonstrate that our new approach can improve the robustness of speech-recognition
systems in reverberant environments, and it performs better than conventional methods.
Keywords: Power envelope restoration, Speech recognition, Modulation transfer function, Powerenvelope inverse ﬁltering
PACS number: 43.72.Dv, 43.60.Cg

1.

[doi:10.1250/ast.29.351]

INTRODUCTION

It is well known that reverberation smears signiﬁcantfeatures of speech so that its quality and intelligibility are
degraded during communication. Restoring the original
clean speech from the observed reverberant speech is,therefore, an important issue in various kinds of realspeech-signal-processing applications, e.g., speech enhancement, hearing improvement, and automatic speech
recognition (ASR). The ultimate goal of our work is toconstruct a blind method of speech dereverberation that can
restore speech signals from reverberant speech withoute-mail: xugang@jaist.ac.jp
e-mail: unoki@jaist.ac.jpze-mail: akagi@jaist.ac.jp
yhaving to measure the impulse response of room acoustics,
and that causes less loss due to the reverberation in the
speech intelligibility and recognition rate.
Traditional methods such as spectral subtraction,
Wiener ﬁltering, and Bayesian estimation have been widelyused [1–3] to improve speech quality and intelligibility
when there is additive noise. These make use of diﬀerent
statistical properties of speech and noise to reduce noise
components and to enhance the speech itself. Reverberation can generally be regarded as the convolution
processing of acoustic speech and room acoustics. The
temporal and spectral structures of speech in a reverberant
environment are distorted by stochastic reverberation
caused by the room-reﬂection characteristics, of walls,
ﬂoors, and ceilings. It is diﬃcult to distinguish clean-speech
signals in a reverberant environment by using the statistical351

Acoust. Sci. & Tech. 29, 6 (2008)

properties of the original speech and of the reﬂected speech
because the speech is degraded by propagating alone
multiple paths. Thus, traditional methods of reducing noise
do not work well in reverberant environments.
Several algorithms for reducing convolution distortion
have been proposed [4–7]. The two most well known are
cepstral mean normalization (CMN) [8] and relative
spectral (RASTA) ﬁltering [9]. These can eﬀectively
reduce the distortions caused by short-term convolution
channels, e.g., microphones, and telephone-transmission
channels. In actual room acoustics, the reverberation time
is far longer, and the properties of reverberant environments are that they are both time and spatially variant.
Several dereverberation algorithms using single ormultiple microphones have been proposed for solving
the room-reverberation problem [7,10–12]. In these approaches, the basic principle of dereverberation is tomeasure the impulse response of room acoustics orpropagation channels, and then use inverse ﬁltering toobtain dereverberated speech [10]. However, these methods require the impulse response of room acoustics foreach dereverberation process to be remeasured if theconditions for room acoustics change.
Blind dereverberation, which does not need the impulse
response of room acoustics to be measured, is preferred
for real applications. One possible way of utilizing blinddereverberation is to use speech characteristics. For
example, the harmonic structure of speech can be used
[13,14]. This method needs the fundamental frequency
from reverberant speech to be accurately estimated, which
is diﬃcult [15], and it does not seem to restore theconsonant (nonharmonic) parts in speech.
In this study, we utilized the characteristics of speech
and the impulse response functions of reverberant environments for speech dereverberation. Speech signals arehighly temporally modulated in amplitude, and most of
their intelligibility information is encoded in the temporalmodulation envelope of all frequency bands [16]. Thismeans that we need to restore the temporal modulated
envelope of clean speech from reverberant speech torestore clean speech for recognition.
In the concept underlying the modulation transfer
function (MTF), the impulse response of room acoustics
is assumed to be a random variable with properties of
exponential-decay temporally modulated and white-noise
carriers [17,18]. In addition, in MTF-based speech dereverberation [7,19–21], the same assumption as for room
acoustics is used and the speech signal is assumed to be arandom variable with properties of temporally modulated
and white noise carriers in each frequency band. On thebasis of the results of stochastical analysis of the signals,
the sub-band power envelope of reverberant speech can beexactly represented as the convolution between the sub-352

band power envelope of clean speech and the powerenvelope of the impulse response of room acoustics. To
obtain sub-band power envelopes of clean speech, only
inverse MTF (IMTF) ﬁltering is needed because of therelationships between the power envelopes of sub-band
reverberant speech, of sub-band clean speech, and of theimpulse response of room acoustics. Therefore, this methodof restoration does not need the impulse response ofroom acoustics to be measured to derive inverse ﬁltering[21,22].
We previously proposed a sub-band power envelope
inverse ﬁltering method based on the MTF concept[21–23]. We tested its eﬀectiveness in restoring the
temporal power envelopes of reverberant signals using
correlation and SNR measurements [21,22]. These tests
demonstrated that the proposed method improves theaccuracy of power-envelope restoration and improves
speech intelligibility [23]. We also conducted a preliminary
test on its capability to act as a front-end processor forspeech recognition in artiﬁcial reverberant environments[24], and we found that it was extremely eﬀective.
However, we have not yet tested its eﬀectiveness as afront-end processor for speech recognition in real reverberant environments. We evaluated how well the proposed
method performed in real reverberant environments,
including multipurpose halls, classic concert halls, lecturerooms, churches, event halls, and speech halls. In addition,
we compared the new approach with some traditional
front-end processes, including auditory-ﬁlter band processing, as well as CMN and RASTA processing.
The paper is organized as follows. Chapter 2 describes
the underlying concept and the model for restoring the
MTF-based sub-band power envelope. Chapter 3 describes
how features are extracted for speech recognition.
Chapter 4 describes the recognition experiments we
conducted in artiﬁcial reverberant environments, andChapter 5 describes the ones we undertook in real
reverberant environments. In Chapter 6, we summarize
the key points, discuss the improvements that are needed,and brieﬂy describe future work.

2.

CONCEPT, MODEL, AND ALGORITHM
FOR RESTORING MTF-BASEDSUB BAND POWER ENVELOPE

2.1. Concept and Model
The MTF concept was proposed by Houtgast andSteeneken [17] to account for the relationship between thetransfer function in an enclosure in terms of input andoutput signal envelopes and the characteristics of theenclosure such as reverberation. This concept was introduced as a measure in room acoustics for assessing theeﬀect of an enclosure on speech intelligibility [17]. The
complex MTF is deﬁned [18] asX. LU et al.: MTF-BASED BLIND RESTORATION FOR ASR

Z1
Mð!Þ ¼0h2 ðtÞ expðj!tÞdt
Z1;
h2 ðtÞdt

ð1Þ

0where hðtÞ is the impulse response of the room and ! isthe radian frequency. For room acoustics, a well-known
stochastic approximation of the impulse response is deﬁned[18] ashðtÞ ¼ eh ðtÞn1 ðtÞ ¼ a expð6:9t=TR Þn1 ðtÞ;ð2Þ

where eh ðtÞ is the exponential decay temporal envelope, a
is a constant amplitude, TR is the reverberant time deﬁned
as the time required for the power of hðtÞ to decay by
60 dB, and n1 ðtÞ is white noise as a random variable(uncorrelated carrier) [21].
The corresponding MTF is obtained using" #1=2
TR 2
jMð!Þj ¼ 1 þ !
:
ð3Þ
13:8
For modulation frequency ! of the temporal envelope,
Eq. (3) can be regarded as the modulation index, i.e., thedegree of relative ﬂuctuation in the normalized amplitudewith respect to the modulation frequency. On the basis of
this characteristic, TR can be predicted from a speciﬁc
modulation frequency by using the MTF.
We modeled what eﬀect room acoustics has on speech
signals on the basis of the MTF concept. The convolution
distortion in each sub-band is written asyn ðtÞ ¼ xn ðtÞ  hðtÞ;n ¼ 1; 2;    ; N;ð4Þ

where yn ðtÞ and xn ðtÞ correspond to the reverberant andclean speech signals in the sub-band, n is the sub-band
index, and N is the total number of sub-bands. Using the
temporal modulation properties of the speech signal, we
model the sub-band speech, xn ðtÞ, as
xn ðtÞ ¼ ex;n ðtÞn2 ðtÞ:ð5ÞThe temporal envelope of sub-band n is ex;n ðtÞ. In Eqs. (2)and (5), n1 ðtÞ and n2 ðtÞ are mutually independent randomvariables that satisfy
hnk ðtÞnk ðt  Þi ¼ ðÞ;

k ¼ 1; 2;ð6Þ

where hi is the ensemble average operator. Using Eqs. (4)–(6), we can calculate the power envelope of yn ðtÞ as
hy2n ðtÞi ¼ e2y;n ðtÞ ¼ e2x;n ðtÞ  e2h ðtÞ

ð7Þ(for details, see Appendix in [21]). This equation showsthat the restoration of e2x;n ðtÞ can be completed bydeconvoluting e2y;n ðtÞ with e2h ðtÞ. To cope with these signals
in computer simulation, the variables are transformed froma continuous signal into a discrete signal on the basis ofsampling theorems, such as e2x;n ½m, e2h ½m, e2y;n ½m, x½m,

h½m, and y½m (m is the number of samples). The transfer
functions of power envelopes Ex ðzÞ, Eh ðzÞ, and Ey ðzÞ areassumed to be the respective z-transforms of e2x ½m, e2h ½m,and e2y ½m. The input-output relationship for deconvolution
can be represented as
Ex;n ðzÞ ¼

Ey;n ðzÞ
Eh ðzÞ

 
Ey;n ðzÞ
13:8
1  exp 
z1 ;
¼
a2
TR;n  fs

ð8Þ

where fs is the sampling frequency. The power envelopeof sub-band signal e2x;n ðtÞ can be restored using theinverse z-transform of Ex;n ðzÞ. In Eq. (8), we only need toestimate parameters TR;n and a. Here, the parameter ofthe inverse MTF ﬁlter related to reverberant time TR;n isassumed to be a function of n since it is dependent on the
sub-band, and is independently estimated from each subband.
2.2. Algorithm
The algorithm for inverse ﬁltering of the sub-band
power envelope was developed on the basis of the analysis
above. The processing scheme for inverse ﬁltering of the
sub-band power envelope is outlined in Fig. 1. In theprocessing scheme, observed signal yðtÞ (a pre-emphasized
signal of the original signal with a coeﬃcient of 0.97) isdecomposed into a series of frequency sub-bands; envelope
detectors then extract temporal modulation envelopes
e2y;n ðtÞ. Considering the co-modulation characteristics ofspeech signals in the sub-bands [22], we deliberately
designed a series of FIR-type band-pass ﬁlters with aconstant bandwidth (100 Hz was chosen in this study) for
the decomposition (see Subchapter 4.1 for bandwidth
selection). Thus, this ﬁlterbank is referred to as aconstant-bandwidth ﬁlterbank (CBFB) in this paper. The
extracted envelopes are used for inverse ﬁltering of thepower envelope, which is controlled using the estimatedparameters of TR;n and a (referred to as T^R;n and a^). The
ﬁnal output is the restored or dereverberated powerenvelope, e^2x;n ðtÞ, for all sub-bands. The implementation
is detailed in the three steps in Subsections 2.2.1, 2.2.2,
and 2.2.3.
2.2.1. Power envelope extraction
The power envelopes in the sub-bands are extracted by
low-pass ﬁltering of the Hilbert transform of the sub-band
signals [21,22].
e^2y;n ðtÞ ¼ LPF½jyn ðtÞ þ jHilbertðyn ðtÞÞj2 ð9ÞHere LPF[] is a low-pass ﬁltering operator and Hilbert()is the Hilbert transform. We set the cut-oﬀ frequency ofthe low-pass ﬁltering to 20 Hz to retain most of theimportant modulation information for speech perception
[22].

353

Acoust. Sci. & Tech. 29, 6 (2008)
#1
y1(t)
.
.
.

Reverberant
signaly(t)Analysisblock(Filterbank)

Power envelope
restoration^ex,12 (t).
.
.

Power envelope restoration

#n
yn(t)
.
.
.

#N
yN(t)

Power envelope
extraction2ey,n(t)Power envelope
inverse-filtering
Parameter
estimation ^ ^a, TR^ex,n2 (t).
.
.

Power envelope
restoration

Recovered
power envelope

2
^ex,N
(t)

Fig. 1 Sub-band power envelope method of inverse ﬁltering.

2.2.2. Parameter estimation
The TR;n and a in Eq. (8) are estimated using Unoki
et al.’s formulas [21]:

Z T
 


ð10Þ
T^R;n ¼ max arg minminðe^2x;n;TR;n ðtÞ; 0Þdt ;
TR;n

and

0

sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ZT

 ﬃ
13:8t
a^ ¼ 1
exp 
dt;
T^R;n
0

ð11Þ

where T is the signal duration, and e^2x;n;TR;n ðtÞ represents thecandidates of the restored power envelope as a functionof TR;n . The reverberant time is constrained as TR;n;min <TR;n < TR;n;max . TR;n;min and TR;n;max are the lower and upperbounds of TR;n (the former was set to 0 s and the latter to
3 s in our study). Estimating the reverberant time using
Eq. (10) means ﬁnding the maximum argument of TR;nfrom a time point to obtain the minimum area of theestimated inverse power envelope with a constraint of noless than zero. Equations (10) and (11) are described indetail elsewhere [21,22].
2.2.3. Power envelope inverse ﬁltering
After the power envelopes (e2y;n ðtÞ) and the parametersof room acoustics (T^R;n and a^) are obtained, the powerenvelopes are inverse ﬁltered using Eq. (8) to restore thepower envelopes of dereverberated speech in the sub-bands(e2x;n ðtÞ). Here, the restored power envelope of the dereverberated speech in a sub-band is denoted as e^2x;n ðtÞ.
Figure 2 shows an example of the eﬀect of restoring
the sub-band power envelope on reverberant speech. The
stimulus was a Japanese sentence (/aikawarazu/) utteredby a male speaker (panel (a)), and reverberant speechoccurred when TR ¼ 1:0 s (panel (b)). The power envelopes of only odd-numbered channels are plotted in thisﬁgure (channels #1, #3, #5, . . ., #39). All pairs of the powerenvelopes (solid and dashed lines) have also been plottedby normalizing them to qualitatively compare matches
between the power envelopes of the original and therestored envelopes. Comparing the sub-band envelopes inpanels (c) and (d) in Fig. 2, we can see that, based on the354

processing procedures, the sub-band power envelope ofreverberant speech can be restored to be close to that ofclean speech.
We have already investigated the restoration accuracy
using two methods. The ﬁrst was the correlation between
the restored sub-band power envelopes of reverberant
speech and the sub-band power envelope of original clean
speech, while the second was the SNR, where S is theoriginal sub-band power envelope, and N is the diﬀerence
between the original and the restored sub-band powerenvelope (see Eqs. (14) and (15) in [21,22]). Many
improvements have been reported for both evaluations(see Fig. 10 in [22] for details). At the same time, we foundthat over- and under estimating the reverberant time does
not optimally restore the temporal power envelope based on
the MTF concept (see Appendix I for a further illustrationof the eﬀect of over- and under estimating of TR;n ).

3.

FEATURE EXTRACTION FOR
SPEECH RECOGNITION

We tested the eﬀectiveness of the proposed algorithm
for dereverberation as a front-end processor for ASR ofreverberant speech. We used clean speech from the
AURORA-2J database as the speech material [25], and
8,840 clean speech sentences to train the acoustic models.
We used 1,001 clean speech sentences to produce
reverberant speech to test recognition in reverberant
environments by convolving the speech signals with theimpulse responses of room acoustics. As the sampling
frequency, fs , was 8 kHz, we used 40 sub-band channels(N ¼ 40) to cover the frequency region from 0 to 4 kHz.
After the restored power envelopes had been obtained fromthe processing blocks represented in Fig. 1, the speechfeature was further extracted as illustrated in Fig. 3.
In Fig. 3, the ﬁrst block is for smoothing which
comprises frame integration and log compression. Because
the inverse ﬁltering of power envelopes is a high-passprocess, low-pass ﬁltering with a forgotten parameter, (in the region between 0 and 1), was used to smooth theenvelope dips in each sub-band:

X. LU et al.: MTF-BASED BLIND RESTORATION FOR ASR
1Restored
power envelopes

(a)

x(t)

0.5
0

^2

ex,1(t)#12(t)e^ x,n#nSmoothing+ Frame integration+ Log compression

–0.5
–1
1

DCT

(b)
Feature vector

y(t)

0.5
0

^2

ex,N(t)

–0.5
–1

0

0.5

1

1.5

40

Channel number

Fig. 3 Extraction of speech features based on restored
power envelope in all sub-bands.

compression was carried out. The discrete cosine transform(DCT) was used for dimensional decorrelation. The ﬁrst 12
dimensions of the decorrelated log power spectrum wereused (the zeroth-order coeﬃcient was discarded). Combining the log power energies, we obtained 13-dimensional
static feature sets. Together with their ﬁrst- and secondorder delta dynamic values, 39-dimensional feature vectors
were formed. The acoustic models consist of ten digits, onesilence and short-pause HMM models which are the sameas those used in the AURORA-2J experiments [25]. Eachdigit model had 18 states with 16 output distributions. The
silence model had ﬁve states with three distributions, andthe short-pause model had three states with one distribution. Each distribution of digits had 20 Gaussian mixtures,
while those of the silence and short-pause models had 36
Gaussian mixtures. The HTK speech toolkit [26] was used
for training the HMM acoustic models.

30
25
20
15
10
5
0
40

(d)

35
30
Channel number

Smoothing+ Frame integration+ Log compression

(c)

35

25
20

4. RECOGNITION EXPERIMENTS
IN ARTIFICIAL REVERBERANTENVIRONMENTS

15
10
5
0

#N

0

0.5

1

1.5

Time (s)Fig. 2 Eﬀect of restoring temporal envelope by inverse
MTF ﬁltering: (a) clean speech, (b) reverberant speechwith TR ¼ 1:0 s, (c) no dereverberation processing(solid lines), and (d) restored envelope using inverse
ﬁltering (solid lines). The dotted lines correspond to
the power envelopes of the original clean speech.

ex;n ½m ¼  ex;n ½m  1 þ ð1  Þe^x;n ½m;

ð12Þ

where e^x;n ½m is the original restored sub-band powerenvelope, and ex;n ½m is the smoothed output. In this study,
we set  to 0.98. To integrate the frames, we used a 32 ms
frame length with a Hamming window and a frame rate of
16 ms. After the integrated spectrum was obtained, log

As previously mentioned, we tested the recognition of
our proposed method in artiﬁcial reverberant environments
by using 1,001 clean speech sentences to produce reverberant speech. The speech signals were convolved with theartiﬁcial impulse responses of room acoustics (produced
using Eq. (2)) with a reverberation time of 0.0, 0.2, 0.4,
0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8 or 2.0 s. In total, we used
1,001 clean speech signals and 1;001  10 reverberant
speech signals.
For comparison, we also tested the performance ofa conventional method of feature extraction based on acomputational auditory model under the same conditions.
There are two steps in processing by conventional feature
extraction: extraction of the sub-band power envelope
using the auditory ﬁlterbank illustrated in Fig. 4, andpost-processing of speech feature extraction on the basis of
the sub-band power envelopes using the same process as
illustrated in Fig. 3.

355

Acoust. Sci. & Tech. 29, 6 (2008)
y1(t)
.
.
.

Reverberant
signaly(t)Gammatone
auditory
filterbank

Power envelope
estimation
Power envelope estimation

#n

y (t)

. n
.
.

#N

yN(t)

Half-wave
rectifying
(HWR)

100

^2

ey,1(t).
.
.

Low-pass filtering+
Squared processing^2

ey,n(t).
.
.
Power envelope
estimation

CQFB
CBFB
CBFB_CMNCBFB_RASTACBFB_IMTF

90^2

ey,N(t)Recognition rate (%)#1

Fig. 4 Extraction of sub-band power envelope based on
auditory ﬁlterbank.

(a)
80

70

60

50

40

30

0

0.5

1

1.5

2

Reverberation time, TR (s)As seen in Fig. 4, the speech signal is ﬁrst decomposedusing a gammatone auditory ﬁlterbank with an equivalent
rectangular bandwidth (ERB). Half-wave rectiﬁcation andlow-pass ﬁltering are then used to extract the sub-band
temporal amplitude envelopes. The power envelopes are
obtained by square processing of the amplitude envelopes.
The ﬁlterbank can be regarded as a constant-Q ﬁlterbank(CQFB). On the basis of the sub-band power envelopes,
we obtain the auditory cepstral feature vector, which isdenoted as CQFB, using the processing block in Fig. 3. In
our preliminary experiments on speech recognition, we ﬁndthat CQFB exhibits equivalent performance to or slightly
better performance than the Mel Frequency Cepstral
Coeﬃcient (MFCC) representation. Considering that our
new approach involves sub-band ﬁltering and extraction
processing of the temporal envelope, we choose theperformance of CQFB as a baseline for comparison. Twoconventional post-processing methods, i.e., RASTA ﬁltering [9] and CMN [8], are also implemented in this study
to deal with convolution distortion. In our study, the twoprocesses are used on utterance-based cepstral temporal
trajectories. Consequently, the features extracted are
denoted here as Fea RASTA and Fea CMN (where ‘‘Fea’’is either CQFB or CBFB).

reported elsewhere [19,20]. Therefore, in our MTF-based
dereverberation experiments, we used band-pass ﬁlterswith a 100 Hz bandwidth. In addition, we compared theresults of recognition using CQFB and CBFB, and foundthat CBFB outperformed CQFB [27].

4.1. Eﬀect of Bandwidth of Band-Pass Filtering
Constant-Q band-pass-ﬁltering (ERB or Mel)-based
extraction of features is widely accepted as being morerobust under most additive noise conditions than constantband-pass ﬁltering. However, because our MTF-based
dereverberation is based on the inverse process of the
sub-band power envelope, this envelope should have comodulation characteristics in the sub-bands while satisfying the MTF concept. We carefully choose the bandwidthby considering the trade-oﬀ in their characteristics [22,23].
In an earlier experiment, we tested what the eﬀect of using
ERBs of gammatone auditory ﬁlters (i.e., constant-Q) andconstant ﬁlter bandwidths would have on recognition [24].
We found that a constant bandwidth of 100 Hz is moresuitable for satisfying the envelope co-modulation properties and the MTF concept. This result is consistent with that

4.2. Comparison with Conventional Feature Extraction Methods
We simulated speech recognition using many typesof feature vectors, i.e., CQFB, CBFB, CBFB CMN,CBFB RASTA, and CBFB IMTF.
The recognition for short reverberation times (TR <0:2 s) was best, as can be seen from the magniﬁed plot in
Fig. 5(b). As seen in the ﬁgure, the speech-recognition
rate decreased as the reverberation time increased; the rateof decrease was particularly high when the reverberation
time was long (TR > 0:2 s). When it was short (TR < 0:2 s),
all the features performed well (recognition rate > 97%).
The CBFB-based feature performed better for longreverberant times, TR > 0:15 s, and slightly worse for shortreverberant times, TR < 0:15 s, than the CQFB-based
feature. Also, as shown in Fig. 5, CBFB RASTA per-

356

99
CQFB
CBFB
CBFB_CMNCBFB_RASTACBFB_IMTF

98.8

(b)

Recognition rate (%)

98.6
98.4
98.2
98
97.8
97.6
97.4
97.2
97

0

0.05

0.1

0.15

0.2

Reverberation time, TR (s)Fig. 5 Comparative evaluations of reverberant speech
recognition rates: (a) whole evaluation and (b) close-upof plot in range from 0.0 to 0.2 s.

X. LU et al.: MTF-BASED BLIND RESTORATION FOR ASR

formed worse than CBFB alone. CBFB CMN performed
slightly worse or almost the same as CBFB alone (except
for short reverberant times, TR < 0:2 s). However,
CBFB IMTF consistently improved the performance of
CBFB alone. We also tested how well CQFB CMN,CQFB RASTA, and CQFB IMTF performed. They performed worse than CBFB alone (see Appendix II for the
results). Consequently, we did not use CQFB CMN,CQFB RASTA, or CQFB IMTF for comparison in later
experiments. In addition, we found that adding CMN orRASTA processing to either CQFB or CBFB did not
improve the recognition rate in our experiments, andsometimes even decreased the performance. Therefore, inthis study, the performance of CQFB was used as abaseline. A relative improvement (RI) in performance wasadopted as the measure of the improvement in recognitionin diﬀerent reverberant environments from diﬀerent baselines, and was deﬁned [25] as
RI ¼

ðTRR  BRRÞ 100 ð%Þ;ð1  BRRÞ

ð13Þ

where ‘‘TRR’’ and ‘‘BRR’’ denote the testing recognition
rate and baseline recognition rate. With this deﬁnition,
the proposed CBFB and CBFB IMTF yielded relative
improvements of 28.64% and 35.67% on average (for0:2 s < TR < 2 s), respectively, in the error reduction ratecompared with CQFB.

5.

RECOGNITION EXPERIMENTS IN REALREVERBERANT ENVIRONMENTS

We then tested our method on speech recognition in
many real reverberant environments (43 halls, rooms, andtheaters [28]) under various conditions. The reverberant
speech signals were obtained from the convolutions
between clean speech signals and the impulse responsesof the environments (the sampling rate of the impulse
responses of the environments were sub-sampled to 8 kHz
to adjust it to the sampling rate of the speech database).
The speech corpus, features, and acoustic models were thesame as those used for the artiﬁcial reverberant environments described in Chapter 4. The characteristics of thereverberant environments and the speech recognition rates
are listed in Table 1.
Rooms and halls constructed of diﬀerent materials andwith diﬀerent conﬁgurations have vastly diﬀerent reverberant characteristics. As listed in Table 1, the reverberant
times for rooms and halls in which we conducted our tests
ranged widely, from 0.36 to 3.62 s. The middle-six columns
list speech-recognition rates with the highest rates for eachroom or hall marked in bold. The rightmost column
indicates the relative improvements in the error reduction
rate of the CBFB IMTF feature compared with that of the
CQFB feature. The CQFB feature performs almost thesame as or slightly better than MFCC (on average).
The CBFB-based features outperformed the CQFBbased features. CBFB RASTA and CBFB CMN showed
no improvements in performance compared with that of
CBFB alone. The CBFB IMTF-based feature had thehighest recognition in almost every case. On average,
CBFB and CBFB IMTF had relative improvements of
15.74% and 25.78% compared with CQFB. The table also
indicates how the diﬀerences in the acoustic characteristicsof the various environments aﬀected the speech-recognition rate. There was almost no degradation in speech
recognition for the meeting room, wooden house, livingroom, or movie theater (recognition rate > 90%) because
these environments had originally been designed tominimize reverberant properties.
In contrast, there was signiﬁcant degradation in speech
recognition in the classic concert hall environments,
probably because these had been designed to emphasize
reverberant properties to enhance the audience’s musicalenjoyment. In Table 1, we can see the eﬀects of reverberant conﬁgurations with various reﬂective properties ofroom acoustics, e.g., with or without reﬂective boards,
absorptive boards, and absorptive curtains. In addition, we
can also see the eﬀects of diﬀerent spatial recording
locations and of the distance between the microphone andsound sources had. A more detailed investigation of theseeﬀects is beyond the focus of this study.

6.

DISCUSSION AND CONCLUSION

Our analysis and experiments demonstrated that our
MTF-based sub-band power envelope extraction andinverse ﬁltering algorithm improves the robustness ofspeech recognition for reverberant speech. The results
revealed that (1) constant-Q band-pass processing or
MFCC presented no advantages in improving ASR inreverberant environments. As Shannon and Paliwal [29]pointed out, auditory motivated band-pass processing does
not have any advantages over other types of band-passprocessing. The band-pass ﬁlters in our study weredeliberately designed to fulﬁll the co-modulation propertiesof speech. The results also revealed that (2) considering the
exponential decay properties of an impulse response ina reverberant environment and the temporal modulation
properties of speech, we can estimate the sub-band
temporal power envelope of speech to some degree without
having to measure the impulse response of room acoustics,
thereby improving the ASR of reverberant speech, and that(3) in real reverberant environments, the proposed estimates of the sub-band temporal envelope with inverse
ﬁltering based on dereverberation consistently improves
ASR (25.78% relative improvement on average).
Upon comparing the recognition rates of CBFB IMTF
and CBFB in Table 1, we ﬁnd that adding inverse ﬁltering357

Acoust. Sci. & Tech. 29, 6 (2008)
Table 1 Reverberant speech recognition rates (%) in actual reverberant environments. IRdata No. indicates File No. in
SMILE2004 [28]. The reverberation time, TR , was determined as the average of all TR s on the transfer function at 125 Hz
to 8 kHz in octave frequencies. ‘‘RI CQFB’’ and ‘‘RI CBFB’’ mean the relative improvement in the error reduction rate of
the CBFB IMTF feature compared with those of CQFB and CBFB features. MPH: Multi-purpose hall; CCH: Classic
concert hall; GSH: General speech hall, RB: Reﬂective board, AB: Absorptive board, AC: Absorptive curtain.
Room condition (Impulse response)

IRdata
No.

TR
(s)

MFCC

CQFB

CBFB

CBFB
CMN

CBFB
RASTA

CBFB
IMTF

RI
CQFB

RI
CBFB

MPH 1 (with RB)(capacity: 2,000 m3 )MPH 1 (without RB)
MPH 2 (with RB)(capacity: 5,700 m3 )MPH 2 (without RB)
MPH 3 (with RB)(capacity: 7,200 m3 )MPH 3 (without RB)
MPH 4 (with AB)(capacity: 12,000 m3 )MPH 4 (without AB)
MPH 5 (capacity: 14,000 m3 )MPH 6 (capacity: 19,000 m3 )

301
302
303
304
305
306
307
308
319
321

1.09
0.80
1.44
1.04
1.93
1.35
1.42
1.54
1.47
2.16

42.55
55.39
32.88
39.70
30.70
42.12
55.70
52.44
46.55
40.13

45.56
54.31
36.60
43.51
33.40
43.48
55.07
53.42
47.28
42.83

52.44
68.52
40.62
47.56
33.80
46.52
69.63
67.02
61.38
49.95

57.63
71.85
39.70
45.49
35.31
53.42
74.24
71.08
59.84
49.43

48.51
66.17
32.64
36.20
31.26
47.50
71.05
66.78
54.71
47.99

60.30
74.33
45.41
52.38
39.31
54.19
75.87
73.10
64.04
54.49

27.08
43.82
13.90
15.70
8.87
18.95
46.29
42.25
31.79
20.40

16.53
18.46
8.07
9.19
8.32
14.34
20.55
18.44
6.89
9.07

CCH 1 (capacity: 5,600 m3 )CCH 1 (d ¼ 6 m)
CCH 1 (d ¼ 11 m)
CCH 1 (d ¼ 15 m)
CCH 1 (d ¼ 19 m)
CCH 2 (capacity: 6,100 m3 )CCH 3 (capacity: 20,000 m3 )CCH 4 (with AC)(capacity: 7,100 m3 )CCH 4 (without AC)
CCH 5 (capacity: 17,000 m3 )CCH 6 (1F front) (capacity: 17,000 m3 )CCH 6 (2F side)
CCH 6 (3F)

309
310
311
312
313
314
315
316
317
323
324
325
326

2.35
2.34
2.35
2.39
2.38
1.14
1.96
1.92
2.55
2.32
1.77
1.74
1.69

27.72
30.09
30.40
30.58
27.82
40.34
35.00
41.23
34.33
31.78
37.73
40.13
34.73

34.20
35.65
35.22
35.37
33.93
44.34
36.81
41.42
36.72
37.70
41.42
44.18
38.23

35.19
39.88
37.67
39.73
36.17
50.60
37.73
50.02
41.97
38.29
43.57
47.87
44.34

33.50
37.03
35.34
38.44
34.30
58.12
42.80
49.95
41.14
34.85
42.55
46.27
43.11

28.92
33.22
33.19
35.55
32.36
49.59
39.12
46.15
37.15
32.58
38.38
42.25
41.42

35.92
42.74
43.17
45.47
40.56
59.84
46.33
54.38
44.43
44.09
53.45
55.14
52.69

2.61
11.02
12.27
15.63
10.03
27.85
15.07
22.12
12.18
10.19
20.54
19.63
23.41

1.13
4.76
8.82
9.52
6.88
18.17
13.81
8.72
4.24
9.40
17.51
13.95
15.00

Lecture room (with ﬂatter echo)
Theater hall (capacity: 3,900 m3 )Meeting room (capacity: 130 m3 )Lecture room (capacity: 400 m3 )Lecture room (capacity: 2,400 m3 )GSH (capacity: 11,000 m3 )Church 1 (capacity: 1,200 m3 )Church 2 (capacity: 3,200 m3 )Event hall 1 (capacity: 28,000 m3 )Event hall 2 (capacity: 41,000 m3 )Gym 1 (capacity: 12,000 m3 )Gym 2 (capacity: 29,000 m3 )Living room (wooden)(capacity: 110 m3 )Movie theater (capacity: 560 m3 )Antrum (capacity: 4,000 m3 )Tunnel (capacity: 5,900 m3 , length: 120 m)
Concourse in train station
GSH 2 (1F front)
GSH 2 (1F center)
GSH 2 (1F balcony)

201
318
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418

1.36
0.85
0.62
1.12
1.09
1.54
0.71
1.30
3.03
3.62
2.82
1.70
0.36
0.38
1.57
2.72
1.95
1.53
1.49
1.40

46.76
46.24
77.43
55.85
57.48
40.44
57.35
33.71
27.51
28.77
21.61
32.51
89.81
88.36
35.19
28.52
36.66
38.26
34.26
39.73

45.72
48.82
72.24
53.18
51.30
44.89
56.95
37.21
31.19
32.98
26.59
37.33
86.40
84.22
36.91
25.05
39.64
41.45
37.67
39.05

60.85
60.55
89.10
70.83
68.35
51.58
70.34
41.42
33.40
35.62
29.08
39.98
98.31
93.49
39.70
25.33
44.06
48.33
45.13
54.41

70.31
60.39
91.25
81.12
83.97
46.58
76.60
40.87
33.40
37.27
27.88
41.60
96.75
95.95
43.97
26.76
46.18
46.88
44.98
59.81

67.58
53.39
89.16
78.75
80.75
44.55
72.43
30.52
30.80
34.88
25.39
36.29
95.30
92.85
36.08
35.06
34.48
42.80
41.26
56.19

68.53
63.68
91.62
80.32
78.85
54.34
77.56
42.49
36.87
41.63
30.09
48.23
96.90
93.18
48.60
33.87
45.93
56.13
51.77
65.18

42.02
29.03
69.81
57.97
56.57
17.15
47.87
8.41
8.25
12.91
4.77
17.39
77.21
56.78
18.53
11.77
10.42
25.07
22.62
42.87

19.62
7.93
25.87
32.53
33.18
5.70
24.34
1.83
5.21
9.34
1.42
13.90
83.93
4.76
14.76
11.44
3.34
21.10
12.10
23.62

to CBFB does not greatly improve the recognition rate
(only a 5.77% absolute improvement in the recognition rate
on average). The recognition rates are still low for many
reverberant conditions. This suggests that we must reconsider how some things are handled from both model andimplementation aspects. For example, speech is assumed to
be a temporal envelope modulated with a Gaussian whitenoise carrier signal. However, the carrier may be non-

358

Gaussian for a real speech signal. Therefore, one direction
to take to achieve improvement is to relax the model
assumptions. In terms of implementation, dereverberationis accomplished using the estimated reverberation time ineach sub-band independently. If there is even a small error
in the estimates, the extracted feature may diﬀer greatly
from the actual ones owning to temporal misalignment
between sub-bands. A more accurate method of estimating

X. LU et al.: MTF-BASED BLIND RESTORATION FOR ASR

the reverberation time is thus needed. We must also ﬁnd amore accurate method of estimating the sub-band temporal
power envelopes because the inverse MTF ﬁltering for
dereverberation is based on these envelopes. We need away to estimate the sub-band temporal power envelope bystochastic signal processing for both Gaussian and nonGaussian white-noise carriers. In our experiments, reverberant speech was obtained by manual convolution
between the speech and the artiﬁcial or real impulse
response of the room acoustics. However, we must
consider real reverberant speech, which should be recorded
in a reverberant environment. In addition, apart fromconvolution-distortion, additive noise in real reverberant
environments may cause speech to degrade.
Finally, as we mentioned in Chapter 1, the impulse
response of a reverberant environment may be timevariant. Our proposed inverse ﬁltering, in Eq. (8), can beused as time-variant ﬁltering if we can estimate theinstantaneous reverberant time. In our current study, thereverberant time was estimated from each utterance. Someutterances in the speech database were long (about 4.2 s),
and others were short (about 0.8 s), and, on average, they
were about 2 s for the data corpus. It is possible to estimate
the reverberant time using a short period of speech, such as
in described [23]. In the future, we intend to deal with real
recorded speech in reverberant environments by adapting
and modifying our MTF-based processing model.

ACKNOWLEDGEMENTS
This work was supported by Grants-in-Aid for Scientiﬁc Research from the Ministry of Education, Culture,
Sports, Science and Technology of Japan (Nos. 18680017
and 18700172). It was also partially supported by theStrategic Information and Communications R&D Promotion Programme (SCOPE; 071705001) of the Ministry ofInternal Aﬀairs and Communications (MIC), Japan. We
would like to thank ATR Spoken Language Translation
Research Laboratories for permitting us to use the
AURORA-2J data.
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based on the MTF concept for speech recognition in359

Acoust. Sci. & Tech. 29, 6 (2008)100

6
Original
Under–est.

(a)

90

2
0
6
Original
Estimated

(b)

e

2
(t)
x,n

4
2

80
75
70
65

55

6
Original
Over–est.

(c)
4
2
(t)
x,n

85

60

0

e

Estimated
Under–est
Over–est

95

Recognition rate (%)

e

2
(t)
x,n

4

50

0.4

0.6

0.8

1

1.2

1.4

1.6

Reverberation time, TR (s)

2
0
0

0.1

0.2

0.3

0.4

0.5
0.6
Time (s)

0.7

0.8

0.9

1

Fig. 7 Eﬀects of over- and under estimated reverberant
time TR on recognition accuracy.
100

APPENDIX I
The eﬀects of over- and under estimation of thereverberant time TR on power envelope restoration is
illustrated in Fig. 6, which has been taken from the 20th
frequency channel of Fig. 2, as an example.
From Fig. 6, one can see that reverberant time T^R;n(0.31 s in this case) estimated using Eq. (10) can guarantee
the best restoration of the power envelope, while under
estimation (about half of T^R;n : 0.16 s) and over estimation(about 1:5  T^R;n : 0.47 s) did not yield best restoration of
the power envelope. In inverse ﬁltering, our algorithm
reveals the best T^R;n for restoring the power envelope sothat the estimated values are not the same for all sub-bands.
Moreover, most of the estimated values are not equal to theoriginal reverberant values. In over estimates, the restored
power envelope in each sub-band is high-pass ﬁltered with360

Recognition rate (%)

reverberant environment,’’ Proc. ICSLP ’06, pp. 2546–2549(2006).
[25] http://sp.shinshu-u.ac.jp/CENSREC/, AURORA-2J database.
[26] The HTK Book (version 3.2), Cambridge University Engineering Department (2002).
[27] X. Lu, M. Unoki and M. Akagi, ‘‘Sub-band temporal envelope
restoration for ASR in reverberation environment,’’ IEICE
Tech. Rep., SP2005-175, pp. 73–78 (2006).
[28] SMILE2004, Sound Material in Living Environment, Architectural Institute of Japan and GIHODO SHUPPAN Co., Ltd.,(2004).
[29] B. J. Shannon and K. K. Paliwal, ‘‘A comparative study of ﬁlter
bank spacing for speech recognition,’’ Proc. Microelectronic
Engineering Research Conf. (2003).

CQFB
CBFB
CQFB_CMNCQFB_RASTACQFB_IMTF

90

(a)

80

70

60

50

40

30

0

0.5

1

1.5

2

Reverberation time, TR (s)
99
CQFB
CBFB
CQFB_CMNCQFB_RASTACQFB_IMTF

98.8

(b)

98.6

Recognition rate (%)

Fig. 6 Eﬀects of over- and under estimation of reverberant time TR on restored power envelope based on(a) under estimated reverberant time (solid line), (b)reverberant time estimated using proposed method(solid line), and (c) over estimated reverberant time(solid line). The dotted line indicates the powerenvelope of original clean speech.

98.4
98.2
98
97.8
97.6
97.4
97.2
97

0

0.05

0.1

0.15

0.2

Reverberation time, T (s)R

Fig. 8 Comparative evaluations of reverberant speechrecognition rates: (a) whole evaluation and (b) close-upof plot in range from 0.0 to 0.2 s.

a higher-end frequency than that used for accurate
estimates, and vice versa for under-estimates. We explained the eﬀect of power envelope restoration in
Subsection 2.2.1. We tested its eﬀect on ASR in overand under-dereverberation, and presented the results in
Fig. 7.

X. LU et al.: MTF-BASED BLIND RESTORATION FOR ASR

From Fig. 7, we can see that the ASR system exhibits
the best performance when the reverberant time isestimated using our new approach [27].

APPENDIX II
The accuracies of speech recognition when using
CQFB, CBFB, CQFB CMN, CQFB RASTA, andCQFB IMTF are plotted in Fig. 8. These are additional

results to those in Fig. 5 and they have the same format.
From the ﬁgure, we can see that CMN and RASTA
processing on CQFB, on average, do not improve recognition any more than does the CQFB alone. Moreover,dereverberation based on the MTF concept in CQFB does
not improve the performance because CQFB processing
does not satisfy the requirements of MTF-based inverse
ﬁltering.

361