JAIST Repository
https://dspace.jaist.ac.jp/

Title

Robust front end processing for speech
recognition in reverberant environments:
Utilization of speech characteristics

Author(s)Petrick, Rico; Lu, Xugang; Unoki, Masashi; Akagi,
Masato; Hoffmann, Ruediger

Citation

Proceedings of INTERSPEECH 2008: 658‑661

Issue Date

2008‑09‑24

Type

Conference Paper

Text version

publisher

URL

http://hdl.handle.net/10119/9985

RightsCopyright (C) 2008 International SpeechCommunication Association. Rico Petrick, Xugang
Lu, Masashi Unoki, Masato Akagi, Ruediger
Hoffmann, Proceedings of INTERSPEECH 2008,
pp.658‑661.

Description

Japan Advanced Institute of Science and Technology

Robust Front End Processing for Speech Recognition in Reverberant
Environments: Utilization of Speech Characteristics
Rico Petrick1 , Xugang Lu2 , Masashi Unoki2 , Masato Akagi2 , Ruediger Hoffmann1
1

Laboratory of Acoustics and Speech Communication, Dresden University of Technology, Germany
2
School of Information Science, Japan Advanced Institute of Science and Technology, Japan[Rico.Petrick,Ruediger.Hoffmann]@ias.et.tu-dresden.de[xugang,unoki,akagi]@jaist.ac.jp

3040

Abstract

25

This paper proposes two methods for robust automatic speech
recognition (ASR) in reverberant environments. Unlike othermethods which mostly apply inverse ﬁltering by blindly estimated room impulse responses to achieve dereverberation, theproposed methods are based on the utilization of the characteristics of speech. The ﬁrst method - Harmonicity based Feature Analysis – takes advantage of the harmonic componentsof speech, which are assumed to be undistorted. The second
method - Temporal Power Envelope Feature Analysis – utilizes
the temporal modulation structure of speech, representing thephoneme level temporal events which contain most intelligibility information. Both methods increase the recognition performance remarkably in a different way. Combining both of themconnects their individual advantages. In order to examine theperformance of utilizing harmonicity and modulation temporal
structure for reverberant ASR, the methods are tested in clean
and reverberant training. As results show, even in strong reverberant conditions both methods obtain practical applicable
performance for reverberant training. In addition, besides testing their performance in dependency on the reverberation time,
their performance considering the speaker-to-microphone distance is tested, which is another new contributions in this paper.
Index Terms: reverberation, robust ASR, harmonicity based
feature analysis, temporal power envelope feature analysis

20
15

r2

R
~
~ 10 log10 (1+ r 2 ) + const

20

~
~

10
5
0

- 60 dB
T 60

t + L(w )
R

10

0

-5
-10
-10
-15
-20

-20

r

0

cm
200

300

400

0.35 0.3

0.25 0.2

0.15 0.1s

0t

Figure 1: S CHROEDER-integral of RIRs, measured at different
SMDs in a SMART room [2] at UPC in Barcelona.

position dependent signal to reverberation ratio (SRR(r), decreasing with the SMD. The SMD, where wD = wR is known
as the reverberation distance rR . Far (approx.: SMD > rR ;wD < wR ) and near (approx.: SMD < rR ; wD > wR )sound ﬁeld behavior can be distinguished. The system between
the speaker and microphone can be described by the room impulse response (RIR) h(t). This also contains direct (hD (t),impulse) and reverberation (hR (t), tail) components. The energy of hR (t) decays exponentially (ideal assumption), whichleads to a linear function in dB, decaying with the velocity−60 dB/T60 . The reverberation time T60 is the most commonlyused property to describe a speciﬁc room. Hence, many ASR
and dereverberation researchers have utilized T60 as the only
parameter to evaluate the quality of their systems in reverberant environments. Figure 1 shows a three-dimensional graphicof the S CHROEDER-integral of RIRs in decibels (LSchr (t, r))measured at varying SMDs. It gives a graphical explanation
why the only use of T60 is insufﬁcient by far to describe dependencies of systems on reverberation; it is only suitable forthe far sound ﬁeld. The investigation of the dependency on the
SMD is a new contribution of the paper.
Inverse-ﬁltering-based dereverberation: Traditional and new
approaches are proposed to solve the dereverberation task (e.g.,[3, 4, 5]). Most dereverberation algorithms are based on blindestimation of the RIR and subsequent ﬁltering of the input signal with the inverse of h(t). However, certain blind estimationof RIRs is a tough task, which is even more problematic while
tracking of changing RIRs (varying speakers location, moving
objects/persons inside the room). Long reverberation times leadto more unstable systems. Adaptation times are mostly far too
long to enable practical applications for command-word ASR.

1. Introduction
Reverberation is one of the major and still unsolved problems
of current research on automatic speech recognition (ASR). It
has a strong degrading effect on the recognition rate (RR) [1].
Methods in the more traditional ﬁeld of noise robustness are notapplicable since reverberation and noise have different effects.
Effects of room acoustics: Inside of rooms reverberation
smears the spectro-temporal structure of speech. The reverberant signal x(t) consists of direct (clean) and reverberant (disturbance) sound components (xD (t) and xR (t)) which add at the
microphone. The direct sound energy (ﬁeld) wD degrades withthe speaker-microphone-distance (SMD) r following wD (r) ≈1/r 2 . The reverberant sound energy (ﬁeld) is position independent (wR (r) ≈ const.; ideal assumption). Both result in aThis work was supported by a Grant-in-Aid for Scientiﬁc Research(No. 18680017) from the Ministry of Education, Culture, Sports, Science, and Technology, Japan, and by the Foundation of German Business (Stiftung der Deutschen Wirtschaft (sdw)). It was also partially
supported by the SCOPE (071705001) of MIC, Japan. Measurementsof the Room Impulse Responses in the SMART room where accomplished during a midterm visiting research period at the TALP ResearchCenter in the UPC Barcelona in collaboration with Carlos Segura andunder the coordination of Prof. Climent Nadeu.

Copyright © 2008 ISCA
Accepted after peer review of full paper

dB
LSchr

658

September 22- 26, Brisbane Australia

8

u

Clean

u

kHz

f 4

v

u

u

v

v

v

2
0

0.2

0.4

0.6

t

s

1

r

r

v

0.2

u

Rev.

u

monic indices nh (multiples of F0 ). Waveform interpolation iscarried out between two nh ’s taking into consideration the typical structure of logarithmized voiced spectra.
(ii) Unvoiced speech is highly reverberated at low frequencies: Unvoiced speech sections, e.g., fricatives, have their mainfeatures in the higher frequency regions. Their lower frequency
regions are highly distorted by reverberation coming from previous voiced sections as shown in Fig. 2. These low frequency
reverberation have high energy compared to the unvoiced features due to the more energetic production process of voiced
speech. Therefore HFA barely suppresses low frequency components, enhancing the structure of the feature vector into a
more unvoiced shape. Some information is lost for unvoiced
wideband signals, but this also applies to the training data. This
processing also recovers the low frequency temporal structureof speech, which is actually the key issue in TPEFA.
(iii) High frequency reverberation is harmless: According to[6] reverberation above 2500 Hz is almost harmless for ASR.
Therefore, HFA involves the previous ideas of (i) voiced and(ii) unvoiced speech only at low frequencies. High frequency
components remain unchanged. The fading interaction between
the original high frequency components and the two types oflow frequency processes is smoothly accomplished by a spectral overlap-and-add. A number of experiments achieved optimal fading parameters [6].
The implementation follows Fig. 3(a). As previously pointed
out, this behavior results in two different types of analysis forvoiced and unvoiced frames, generating the synthetic spectra,
Xs,v (n) and Xs,u (n), which are passed to the MFB.
F0 estimation: Initially the autocorrelation function (ACF)
method is used. Under reverberation this simple approach performs similar compared to advanced methods [11].
VUD: Voiced unvoiced decision (VUD) also uses a simple approach where the mean energy of the harmonic components ofa frame is compared to a dynamically derived threshold [6].
Considering F0 estimation and VUD errors: Using othermore sophisticated approaches for F0 estimation, F0 postprocessing or VUD (e.g., [12]) did not lead to better results of
RR, concluding that these easy approaches appeared as sufﬁcient. Their errors are handled by the model, since errors also
occur while analyzing the training data. Error modeling performs even better for reverberant training (compare the results
in sections 3.1 and 3.2). This is especially the case for VUD
errors, which therefore demand at least a two-Gaussian Mixture Model (two-GMM) Hidden Markov Model (HMM). Due tooccurring VUD errors, two different methods of analysis generating Xs,v (n) and Xs,u (n) can be undertaken for the samephoneme (one for the correct and the second for the incorrect
VUD), forming two distant clusters in the feature space for thesame phoneme. One could argue that analyzing voiced frames
in an unvoiced manner would delete too much information for
discrimination. Only low frequency components are suppressed
but second and third formant information still remains resulting in slightly reduced discrimination. Despite these errors,
the overall processing of HFA increases the performance of the
ASR for reverberant training even more.

v
r
0.4

r
0.6

t

s

1

Figure 2: Schematic illustration of disturbances caused by reverberation (r) for voiced (v) and unvoiced (u) speech.

Dereverberation utilizing speech characteristics: Approaches that do not need to estimate the RIR are preferred
for real ASR systems. Unlike signal enhancement systems
which demands sophisticated speech quality, ASR has the advantage, that only features need to be restored. One possible
way of enhancing features is the utilization of speech properties, which are not affected by reverberation. The authors propose two techniques, each utilizing a different speech property:(I) Harmonicity-based feature analysis (HFA) [6] utilizes two
assumptions of reverberant speech: (a) harmonic components
are assumed to be undisturbed and (b) the low frequency energy of unvoiced sections is most probably reverberation andcan be deleted. (II) Temporal power envelope feature analysis(TPEFA) as similar in [7, 8] utilizes the temporal modulation
structure that remains under reverberant conditions.
Requirements for applicable command word ASR: Applicable ASR in reverberant rooms requires: (a) a robust acceptable recognition rate (RR) (≈ 90% [9]) under typical varying indoor conditions (living/home/ofﬁce environments: 0.3 s< T60 < 1.0 s; 0.5 m < SMD < 4 m), (b) no or real time
adaptation (< 2.0 s), (c) robustness against changes in the
RIR (movements of speakers/object) and (d) feasible numericalcomplexity. Practical applicable dereverberation methods haveto meet these demanding requirements.

2. Utilizing Speech Properties for Rev. ASR
In this paper only a brief overview of implemented ideas of theapplied front-end methods is given. The exact implementations
are described in associated references: (a) CFA (Conventional
Feature Analysis) as used in [10], (b) HFA proposed in detailin [6], (c) TPEFA as used in [7, 8] and (d) HFA+TPEFA combined proposed in this article. Before any of these front-end
methods are evaluated, the model has to be trained applying theappropriate front-end on the training data.
2.1. Conventional Feature Analysis
For comparison subsequent methods have to adapt to the same
conditions as the CFA used in [1, 10]. These involve classical short time Fourier analysis (STFA) followed by a mel
ﬁlterbank (MFB). STFA includes framing of the input signalx(k) (fs = 16 kHz) into x(a, k) (frame index a), windowing
and FFT (N = 512). MFB includes a logarithm and cepstralsmoothing on magnitude spectrum |X(a, n)|, energy derivationand normalization of 30 mel-scaled ﬁlter channels composing afeature vector, x(a). The absolute frame energy is added as the
31st component.

2.3. Temporal Power Envelope Feature Analysis
Recent researches show that most speech intelligibility information is encoded in the temporal modulation envelopes (TMEs)of frequency subbands [13]. Furthermore, as these TMEs arerobust against noise distortion in speech-enhancement systems,
they have to be restored. Following the same idea to enhance
ASR features, techniques such as Relative Spectral Filtering(RASTA) for spectral or cepstral trajectories [14] are proposed.

2.2. Harmonicity-based Feature Analysis
HFA implements three ideas:(i) Harmonic components are assumed to be clean: This
principle is already used in [5]. HFA synthesizes voiced spectra
Xs,v (n) based on the measured harmonic components at har-

659

X(a)
x(a)

0

(a)
CBFB

(b)

Harmonic
Amplitudes

VUD

v/u

100

MFB

XS (a)

%

x(a)

RR

HFA

80
70
60

x1 (k)
x(k)

F(a)
0

Spectral
Synthesis

Xh(a)

xc(k)
xC(k)

Temporal Power Envelope Estimation
HilbertIm( xc(k))
Re( xc(k))

2LPFe^c (k)Mk 160Temporal Power Envelope Estimatione^1 (a)e^c (a) Lc 4

CFA
HFA
TPEFA
HFA+TPEFA

50

Xe^ (a)

MFB

40

x(a)

30

e^C (a)20

Clean
Training

100

Figure 3: Block diagrams of (a) HFA and (b) TPEFA.

T
0

60 ,Test

100T= 0.7 s200cm
SMD Test

60 ,Train= 0.7 sT

SMD Train = 0.6 m
T ,Test = 0.7 s

(a)

(b)

60

400 0

100

200

cm
SMD Test

60 ,Train= 0.7 sSMD Train = 2.8 m
T ,Test = 0.7 s

(c)

60

400 0

100

200

cm
SMD Test

400

Figure 4: RR dependent on SMDTest . Training conditions:
SMDTrain = (a) 0.0 m (clean), (b) 0.6 m and (c) 2.8 m.

For the reverberant conditions the authors investigate and discuss, that the ﬁne temporal structure of speech is smeared
but the large-scale TME structures corresponding to linguistic
events (phonemes) are still retained. However, they are ﬂattened due to reverberation. As some researchers (e.g., [15])show, most speech intelligibility information is distributed in
the TME structures between 2 Hz and 20 Hz. Concluding, occuring higher modulation frequencies can be seen as induced by
distortions as reverberation. According to these assumptions,the implemented TPEFA front-end (Fig. 3(b)) aims to restore
the temporal modulated power envelope (TPE) for frequencies
below 20 Hz. Considering the temporal co-modulation property of speech [7], x(k) is decomposed into C = 64 evenly
distributed frequency bands (channel index c; time domain constant band ﬁlterbank (CBFB), and a bandwidth of 100 Hz according to previous research [7, 8] by the authors). Each subband signal xc (k) can be regarded as a temporal (amplitude)
modulated signal:


k
(1)
xc (n) = α̂c (n) cos ωc + ϕc ,fs

3. Evaluation
This work uses the same evaluation system as described in [1, 6](UASR recognizer, subset of APOLLO corpus [9] consisting of1020 command phrases (each ≈2 s speech) of 17 classes. Twosets of dependency experiments are accomplished:•RR(SMD) in the SMART room environment (Fig. 1)•RR(T 60 ) in rooms for near and far ﬁeld (SMD = 1 m/3 m).
3.1. Evaluation Results for Clean Training•RR(SMD) (Fig. 4 (a)): A strong degradation using CFA canbe observed even after a few cm of SMD. HFA and TPEFA increase the performance over the whole range, closely won by
HFA. HFA+TPEFA performs best and takes advantages of bothmethods. Interesting point: The results adumbrate the reverberation distance of this room (rR ≈1.5,. . . ,2.5 m).
•RR(T 60 ) (Fig. 5 (a1), (a2) (near, far ﬁeld)): CFA again performs poorly, decreasing with increasing T60,Test . HFA gradually improves for clean training, due to some loss of information in the undistorted data (reverberant training compensates
for this effect, as described below). For increasing T60,Test , thedegradation in RR is less than for CFA; → HFA increases the
RR for reverberant conditions. TPEFA enhances the general information properties of speech, increasing the RR already forthe clean case but also over the whole reverberant test range.
HFA+TPEFA leads to a slight drop at clean conditions compared to TPEFA, due to the loss caused by HFA. But for the
more reverberant conditions, the advances of HFA and TPEFA
add again.

where α̂c is the TME, ωc and ϕc are the associated carrier frequency and phase of the c-th subband. To extract TPE êc (k),the squared magnitude of the complex analytical signal xc (k)is derived. xc (k) is composed of xc (k) as the real part and theHilbert transform (Hilbert [·]) of xc (k) as the imaginary part.
Subsequently, êc (k) is low-pass ﬁltered (LPF with a cutoff frequency of 20 Hz according to [15], ref. above):(2)êc (k) = LPF |xc (k) + jHilbert [xc (k)]|2 .
êc (k) is still a time signal. The index c can be seen as a frequency index of a C-channel TPE spectrum for each time index
k. To use this preprocessor for ASR, framing is applied by down
sampling with Mk = Ia (frame interval Ia = 160, the same as
for CFA) resulting in êc (a). No anti-aliasing ﬁlter is needed,because of the previous LPF (20 < fs /(2Ia ) Hz). To compare the performance of TPEFA with the other methods, êc (a)is interfaced with MFB, which requires N/2 frequency bins as
input. Therefore, êc (a) is up-sampled in frequency by Lc = 4(simple trapezoid interpolation).

3.2. Evaluation Results for Reverberant Training
In difference to speech enhancement systems, ASR has the advantage to train models at the disturbing conditions. However, a
dedicated reverberant model usually tends to support the training condition, but drops other conditions (refer CFA in all diagrams). Good behavior is achieved by a method when a training
condition can be used for general test conditions.
•RR(SMD) (Fig. 4 (b), (c)): The model is trained under reverberant conditions of the SMART room at several SMDs. CFA
performs better for the far ﬁeld, but loses RR in the near ﬁeld.
HFA training at SMD = 280 cm achieves the best performance,
although there is a slight decrease for clean case. TPEFA performs signiﬁcantly better than CFA, but the results tend to support the training condition resulting in a loss for clean test data.
HFA+TPEFA is marginally outperformed by HFA, due to the
RR drop of TPEFA at short SMDs.
•RR(T 60 )(Fig. 5 (b) – (d)): Reverberant training conditions atseveral T60 ’s (at SMD = 1 m; far ﬁeld training (3 m) did notlead to good results) are applied. CFA performs better for the

2.4. Combination of HFA and TPEFA
HFA+TPEFA uses the synthesized spectra Xs (a, n) generated
within HFA for resynthesis into short time signals xs (a, k)
applying the Fourier series, which incorporates the original
phase ϕ(X(a, n)). An overlap-and-add algorithm assembles
the HFA-processed time signal as input for the TPEFA. An inverse processing order, i.e., TPEFA before HFA is not possible
since TPEs cannot be resynthesized or retain harmonicity information. For the same reason as HFA also HFA+TPEFA requires
at least a 2-GMM model.

660

100

100

100

%

%

%

%

RR 80

RR 80

RR 80

RR 80

70

70

70

70

60

60

60

60

50

50

50

50

40

40

40

40

30

30

30

20

Clean
Training

10

SMD Test = 1 m

(a1)

0
100

100

30

20

T

= 0.4 s20T

= 1.0 s

20

T

10

SMD Train = 1 m
SMD Test = 1 m

10

SMD Train = 1 m
SMD Test = 1 m

10

SMD Train = 1 m
SMD Test = 1 m

60 ,Train

(b1)

0
100

60 ,Train

(c1)

0
100

%

%

%

%

RR 80

RR 80

RR 80

70

70

70

70

60

60

60

60

50

50

50

50

40

40

40

40

30

30

30

Clean
Training

100

SMD Test = 3 m
0

0.5

(a2)
1
T60,T est

s

2

20

T

10

SMD Train = 1 m
SMD Test = 3 m

060 ,Train0= 0.4 s

0.5

(b2)
1

T60,T ests2

= 2.0 s

(d1)

30

20

T

10

SMD Train = 1 m
SMD Test = 3 m

060 ,Train

0
100

RR 80

20

CFA
HFA
TPEFA
HFA+TPEFA

0

60 ,Train= 1.0 s

0.5

(c2)
1
T60,T est

s

2

20

T

10

SMD Train = 1 m
SMD Test = 3 m

00

60 ,Train

0.5= 2.0 s(d2)
1
T60,T ests

2

Figure 5: Dependency of the RR on T60,Test in near ﬁeld (SMDTest =1 m, top diagrams) and in far ﬁeld (SMDTest =3 m, bottom
diagrams). Applied training conditions: T60,Train = (a) 0.0 s (clean), (b) 0.4 s, (c) 1.0 s and (d) 2.0 s at SMDTrain =1 m.

5. References

particular training condition, but drops the RR for other conditions. HFA increases the RR of CFA and keeps it stable undervarious rev. conditions when training becomes more reverberant. This can especially be observed in Fig. 5(d1) (HFA compared to TPEFA and CFA). TPEFA performs signiﬁcantly better
than CFA but also better than HFA (in most cases). However, it
tends to support the actual training conditions and decreases under different test conditions. HFA+TPEFA again combines theadvantages of HFA and TPEFA (stable vs. high improvement).
T60,Train = 1 s achieves the best overall results (both top andbottom ﬁgures should always be considered for rating).

[1] Petrick, R., Lohde, K., Wolff, M. and Hoffmann, R., ”The harming part of room acoustics for automatic speech recognition,”
Proc. INTERSPEECH2007, pp. 1094–1097, Antwerp, 2007.
[2] Neumann, J., Gasas, J. R., Macho, D., Hidalgo, J. R., ”Integrationof audio-visual sensors and technologies in a smart room,” Personal and Ubiquitous Computing, Springer London, ISSN: 16174909 (print), 1617-4917 (online), April 2007.
[3] Miyoshi, M. and Kaneda, Y., ”Inverse ﬁltering of room acoustics,”
IEEE Trans. ASSP(36), pp. 145–152, 1988.
[4] Gillespie, B. W., Malvar, H. S., and Florencio, D. A., ”Speech
dereverberation via maximum-kurtosis subband adaptive ﬁltering,” Proc. ICASSP2001, pp. 3701–3704, Salt Lake City, 2001.
[5] Nakatani, T., Kinoshita, K., and Mihyoshi, M., ”Harmonicity
based blind dereverberation for single-channel speech signals,”IEEE Trans. ASLP 15(1), pp. 80–95, 2007.
[6] Petrick, R., Lohde, K., Lorenz, M., and Hoffmann, R., ”A new
feature analysis method for robust ASR in reverberant environments based on the harmonic structure of speech,” Proc. EUSIPCO2008, Lausanne, 2008 (accepted).
[7] Unoki, M., Sakata, K., Furukawa, M., and Akagi, M., ”A speech
dereverberation method based on the MTF concept in power envelope restoration,” Acoust. Sci. & Tech., pp. 243–254, 25(4), 2004.
[8] Lu, X., Unoki, M., and Akagi, M., ”Comparative evaluation ofmodulation-transfer-function-based blind restoration of sub-band
power envelopes of speech as a front-end processor for automatic
speech recognition systems,” Acoust. Sci. & Tech., 2008 (in press).
[9] Maase, J., Hirschfeld, D., Koloska, U., Westfeld, T., and Helbig,
J., ”Towards an evaluation standard for speech control concepts
in real-world scenarios,” Proc. EUROSPEECH2003, pp. 1553–
1556, Geneva, 2003.
[10] Hoffmann, R., Eichner, M. and Wolff, M. ”Analysis of verbal andnonverbal acoustic signals with the Dresden UASR system,” In
Esposito, A., et al. (eds.): Verbal and Nonverbal Communication
Behaviours, pp. 200–218, Berlin etc.: Springer, LNAI 4775, 2007.
[11] Petrick, R., Unoki, M., Mittal, A., Segura, C., and Hoffmann, R.,”A comprehensive study on the effects of room reverberation on
fundamental frequency estimation,” Proc. INTERSPEECH2008,
Brisbane, 2008 (accepted).
[12] Luengo, I., Saratxaga, I., Navas, E., Hermaez, I., Sanchez, andSainz, J., ”Evaluation of pitch detection algorithms under realcondition,” Proc. ICASSP2007, pp. 1057–1060, Honolulu, 2007.
[13] Shannon, R. V., Zeng, F., Kamath, V., Wygonski, J., and Ekelid,M., ”Speech recognition with primarily temporal cues,” Science,
270, pp. 303–304, 1995.
[14] Hermansky, H., Morgan, N., and Hirsch, H. G., ”Recognition of
speech in additive and convolutional noise based on RASTA spectral processing,” ICASSP’93, pp. 83–86, 1993.
[15] Dau, T., ”Modeling auditory processing of amplitude modulation,” Ph.D. Thesis, ISBN 3-8142-0570-7, 1996.

4. Conclusions
Comprehensive recognition experiments show that both applied
methods, HFA and TPEFA, can improve recognition. Although
the RR is drastically increased (e.g., from 35% up to 70%) under clean training conditions, the performance is still insufﬁcient for practical considerations (< 90%). Additional reverberant training achieves practical application requirements; also
for varying reverberation conditions. The gain at HFA is causedby harmonic components, which can be considered as clean
and by the deletion of low frequency reverberation in unvoiced
speech, which is highly disturbing. HFA suffers at clean conditions since some information is deleted but stably improves inreverberant condition. TPEFA gains the ASR performance by
information about the temporal envelope modulation, which isa robust information carried by speech also in noisy and reverberant environments. However TPEFA tends to support the applied training condition. The combination HFA+TPEFA takes
advantages of both methods (stable improvement and high improvement) and compensates their weak points. The enhancement of both methods is achieved by emphasizing feature information by characteristic preferences of speech, resulting inhigh practical applicable RRs even in adverse environments. No
adaptation as in current dereverberation approaches is neededleading to real time processing ability, required in command
word recognition applications. A disadvantage of the TPEFA isa high processing load due to the time domain ﬁlter bank, whichcannot be handled by current embedded devices, but will be infuture systems.

661