Blind Noise Suppression for Non-Audible Murmur
Recognition with Stereo Signal Processing
Shunta Ishii, Tomoki Toda, Hiroshi Saruwatari, Sakriani Sakti, Satoshi Nakamura
Graduate School of Information Science, Nara Institute of Science and Technology,
8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan{ shunta-i, tomoki, sawatari, ssakti, s-nakamura }@is.naist.jp

Abstract—In this paper, we propose a blind noise suppression
method for Non-Audible Murmur (NAM) recognition. NAM is avery soft whispered voice detected with NAM microphone, which
is one of the body-conductive microphones. Due to its recording
mechanism, the detected signal suffers from noise caused byspeaker’s movements. In the proposed method using a stereo
signal detected with two NAM microphones, the noise is estimatedwith blind source separation, and then, spectral subtraction is
performed in each channel to reduce the noise. Moreover, channel
selection is performed frame by frame to generate less distortedmonaural NAM signal. Experimental results show that 1) word
accuracy in large vocabulary continuous NAM recognition isdegraded from 69.2% to 53.6% by the noise and 2) it issigniﬁcantly recovered to 63.3% in a simulated situation and58.6% in a real situation with the proposed method.

I. I NTRODUCTION
The explosive spread of portable devices with a lot offunctions makes us realize importance of the development of
natural interfaces to use them. A speech interface is one of
the typical natural interfaces and speech recognition is a key
technology to develop it. Although speech is a convenientmedium, there are actually some situations where we face
difﬁculties in using speech. For example, we would havetrouble privately talking in a crowd; and speaking itself would
sometimes annoy others in quiet environments such as in alibrary. The development of technologies to overcome these
inherent problems of speech is essential.
Recently, silent speech interfaces [1] have attracted attention
as a technology to make speech interfaces more convenient.
They enable speech input to take place without the necessity ofemitting an audible acoustic signal. New sensing devices as alternatives to the air-conductive microphone have been explored
to detect silent speech signals, such as the throat microphone[2], electromyography (EMG) [3], ultrasound imaging [4], andso on. These sensing devices are also effective as noise robust
speech interfaces; e.g., Subramanya et al. [5] have reported
that bone-conducted speech signals can be effectively used toenhance speech sounds under noisy conditions.
As one of the sensing devices to detect silent speech signals,
Nakajima et al. [6] developed a Non-Audible Murmur (NAM)
microphone. NAM is an extremely soft whispered voice,which is so quiet that people around the speaker hardly hearits emitted sound. Placed on the neck below the ear, the NAM

978-1-4673-0367-5/11/$26.00 ©2011 IEEE494

microphone is capable of detecting extremely soft speech suchas NAM from the skin through only the soft tissues of thehead. It is capable of high-quality body-conductive recording
and its usability is better than those of other devices such as
EMG or ultrasound systems. There have been several attempts
to develop a NAM recognition system by modeling acoustic
characteristics of NAM [7], [8], [9], which are very different
from those of normal speech.
In the conventional studies of NAM recognition, the speakers tried maintaining their positions as stably as possible
during speaking to keep a setting condition of the NAM
microphone as constantly as possible. However, this constraint
will not be enforced in a real situation; the speaker often moves
in speaking. Since the detected signal with NAM microphone
is sensitive to the setting condition of the NAM microphonesuch as the pressure to attach the NAM microphone, noise iseasily generated when the speaker moves. For example, when
the speaker moves his/her head to look away, noticeable noise
is generated if the attachment plane of the NAM microphone
is rubbed by the skin. The NAM signal easily suffers fromthe generated noise and NAM recognition performance issigniﬁcantly degraded. Since the generated noise is nonstationary and its frequency components widely overlap thoseof the NAM signal, it is not straightforward to suppress it.
In this paper, we propose a blind noise suppression method
using stereo signal processing. A stereo signal is detectedwith two NAM microphones attached below the both ears.
Blind Spatial Subtraction Array (BSSA) [10] is used to estimate the non-stationary noise signal by cancelling a target
signal (i.e., the NAM signal) with the beam forming process.
In BSSA, unsupervised optimization of the beam former isperformed with independent component analysis (ICA). And
then, spectral subtraction (SS) [11] is performed with theestimated noise signal to reduce the noise in each channel.
Moreover, to generate less distorted monaural NAM signal
to be used in NAM recognition, frame-wise channel selection
based on a time-varying signal-to-noise ratio (SNR) calculatedwith the estimated noise signal is implemented. We conduct
experimental evaluations in large vocabulary continuous NAM
recognition to show that the proposed method signiﬁcantly
improves NAM recognition performance by suppressing thenoise caused by the speaker’s movements.

ASRU 2011

MuscleAirvibration
Oral cavity
Bone

Skin
Soft silicon
Open condenser
microphone
External
noise proof

Structure of NAM microphone

Fig. 1.

Setting position and structure of NAM microphone.

Fig. 3. Example of waveform and spectrogram of NAM signal when the
speaker moves during speaking.

the effect of noise generated by the speaker’s movements has
been minimized in these studies by asking the speakers tomaintain their positions as stably as possible during speaking.
C. Effect of Speaker’s Movements on NAM Signal
Fig. 2.

Example of waveform and spectrogram of NAM signal.

II. N ON -AUDIBLE M URMUR R ECOGNITION
A. Non-Audible Murmur (NAM)
NAM is deﬁned as the articulated production of respiratory
sounds without using the vocal-fold vibration, which can be
conducted through only the soft tissues of the head without
any obstruction such as bones [6]. NAM is recorded using theNAM microphone attached to the skin surface behind the ear,as shown in Figure 1. In this work, a neckband-type of NAM
microphone [7], which presses NAM microphone against theplace, is used to stably attach it. Because NAM is a particularly
soft whispered voice, it is ampliﬁed with a special ampliﬁer.
Figure 2 shows an example of waveform and spectrogram of
NAM. High-frequency components of NAM are usually not
well observed owing to the mechanisms of body conduction,such as lack of radiation characteristics from lips and effectof low-pass characteristics of the soft tissues.
B. Conventional Work of NAM Recognition
The main difference between a normal speech recognition
system and a NAM recognition system is an acoustic model.
Since the amount of NAM data is still limited, most of theconventional work focused on the development of speakerdependent NAM acoustic models. It has been reported thatmodel adaptation techniques such as Maximum Likelihood
Linear Regression (MLLR) [12] is effective to develop hidden
Markov models (HMMs) for NAM from those for normal
speech [8]. Moreover, the adapted speaker-dependent NAM
acoustic models are further improved by reﬁning the initial
HMMs using NAM data from multiple different speakers [7]and also using many speakers’ normal speech data transformed
into NAM acoustic space [9]. Consequently, around 70% word
accuracy has been achieved for various speakers in large
vocabulary continuous NAM recognition. On the other hand,495

The skin surface and muscles around the place of attachmentof the NAM microphone usually move in conjunction with the
speaker’s movements in particular such as the movements of
his/her head. These movements often change the setting condition of the NAM microphone. Figure 3 shows an example ofwaveform and spectrogram of NAM when the speaker lightly
shakes his head. We can see that the NAM signal is severely
deteriorated by noise caused by the speaker’s movements. The
generated noise is non-stationary and causes substantially large
acoustic differences compared with the NAM signal shown in
Figure 2.
III. B LIND N OISE S UPPRESSION WITH S TEREO S IGNALSFOR NAM R ECOGNITIONIn the proposed method, two NAM microphones are placedon the neck below the both ears to detect a stereo signal.
The stereo signal allows us to use various effective noise
suppression techniques such as the beam forming process.
First, we represent the sound mixing model in the stereo signal
detected with the NAM microphones. And then, the proposed
blind noise suppression method is described.
A. Stereo Signal Modeling
When the speaker does not move, the stereo signal of NAM
without suffering from any non-stationary noises is detected.
A short-time analysis of the detected stereo signal is conductedby frame-by-frame discrete Fourier transform (DFT). The
detected stereo NAM signal s(f, τ ) = [s1 (f, τ ), s2 (f, τ )]consisting of the ﬁrst channel signal s1 (f, τ ) and the second
channel signal s2 (f, τ ) is modeled bys(f, τ )=a(f )s0 (f, τ ),(1)

where  denotes transposition of the vector, f is the frequency bin, and τ is the time index of DFT analysis. A
component of the NAM signal before the body conduction is
given by s0 (f, τ ), which is unobserved. It is linearly ﬁlteredFig. 4. Example of waveform and spectrogram of stereo NAM signal (top:the 1st channel, bottom: the 2nd channel).

Fig. 5. Example of waveform and spectrogram of stereo noise signal causedby speaker’s movement (top: the 1st channel, bottom: the 2nd channel).

with channel-dependent and time-invariant transfer functions
a(f ) = [a1 (f ), a2 (f )] , which are affected by various factors
such as a setting position of the NAM microphone, a settingof the ampliﬁer, and so on. Figure 4 shows an example of thestereo NAM signal. We can see that there are some acoustic
differences between NAM signals in different channels but
they highly correlate to each other. We have conﬁrmed froma result of our preliminary experiment that the NAM signal
in one channel is effectively cancelled by the linearly ﬁltered
NAM signal in the other channel.
Non-stationary noise is generated depending on the
speaker’s movements. Figure 5 shows an example of thestereo noise signal caused by a light shake of the head. The
noise signals in individual channels seem to be synchronizedat some parts but the correlation between them is actually
weak. Therefore, the detected stereo noise signal is modeledby n(f, τ ) = [n1 (f, τ ), n2 (f, τ )] as diffuse noise signals. It
may also be modeled bymoves during speaking in NAM is modeled byn(f, τ ) =b(f, τ )n0 (f, τ ),(2)

where a component of an unobserved noise signal given
by n0 (f, τ ) is linearly ﬁltered by channel-dependent andtime-variant transfer functions b(f, τ ) = [b1 (f, τ ), b2 (f, τ )]affected by the changes of setting conditions of individual
NAM microphones.
Assuming that the non-stationary noise is additive to the
NAM signal, the detected stereo signal when the speaker496

x(f, τ )

a(f )s0 (f, τ ) + n(f, τ ).

(3)

Note that to simplify the mixing process we also assume thatthe speaker’s movements do not change the transfer functiona(f ). It is investigated in Section IV-B whether or not thisassumption is valid.
B. Blind Spatial Subtraction Array (BSSA)
BSSA [10] is a blind noise suppression method based on
two key processes, 1) noise estimation based on the target
signal canceller with an adaptive beam former designed byblind source separation (BSS) based on ICA and 2) SS with theestimated noise signal. This technique is capable of effectively
enhancing the target signal as long as it is well suppressed by
the beam former even if the additive noise signal is diffuse,
which is essentially difﬁcult to be suppressed by the beam
former. These conditions are matched with the mixing process
given by Eq. (3). Moreover, since BSSA is a blind process, wedon’t have to know a position of microphones, the directionof arrival (DOA) of the target signal, and so on. In our case,the position of NAM microphones and the transfer function
in Eq. (1) substantially vary depending on individual speakers
and the setting conditions of NAM microphones. Moreover,
we don’t know the DOA of the NAM signal. Therefore, theblind process is essential in NAM recognition. The proposednoise reduction process based on BSSA for NAM recognitionis shown in Figure 6.

EŽŝƐĞϭEŽŝƐĞƌĞĚƵĐƚŝŽŶƉĂƌƚx1 ( f ,τ )

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&
x ( f ,τ )d 2

EDEŽŝƐĞϮ

'^^
&Ͳ
/

Ϭ

o1 ( f ,τ )

ˆ1 ( f ,τ )W no2 ( f ,τ )nˆ2 ( f ,τ )

/
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d

EŽŝƐĞĞƐƚŝŵĂƚŝŽŶƉĂƌƚ
Fig. 6.

Block diagram of BSSA for NAM recognition.

1) Noise Estimation Process: Frequency domain ICA (FDICA) based on higher-order statistics is used to estimate thenoise signal. The detected stereo mixed-signal is separated
with the complex valued unmixing matrix W ICA (f ) so thatthe output signals o(f, τ ) = [o1 (f, τ ), o2 (f, τ )] become
mutually independent. The output signals are given byo(f, τ )=W ICA (f )x(f, τ ),(4)

where the unmixing matrix W ICA (f ) is determined by minimizing Kullback-Leibler divergence between the joint probability density function p(o(f, τ )) and the marginal probability
density function p(o1 (f, τ ))p(o2 (f, τ )) over a time sequence.
The optimal W ICA (f ) is obtained using the iterative equation:
[i+1]

[i]

W ICA = W ICA (f ) [i]+α I − Φ(o(f, τ ))oH (f, τ )τ W ICA (f ), (5)where α is the step-size parameter, [i] indicates the value of thei-th step in iterations, I is the identity matrix, ·τ denotes thetime-averaging operator, H denotes Hermitian transposition,and Φ(·) is the nonlinear vector function [13]. In this paper,
we determine the unmixing matrix utterance by utterance.
In the mixed signal modeled by Eq. (3), the separation process given by Eq. (4) is not obviously capable of suppressing
the noise signal n(f, τ ). On the other hand, it is capable of
suppressing a component of the NAM signal s0 (f, τ ). In otherwords, it is capable of well estimating a component related to
the noise signal n0 (f, τ ). Therefore, only the noise component
is useful in the output signals. To remove the NAM signal fromthe output signals, the following “noise-only” signal vectoro(n) (f, τ ) is constructed:o(n) (f, τ ) = [0, o2 (f, τ )] .

(6)

An initial matrix of W ICA is designed so that o2 (f, τ )becomes the noise component. The DOA analysis may also
be used to determine which is the noise component, o1 (f, τ )or o2 (f, τ ) [13]. And then, the projection back (PB) process[14] is performed to remove the ambiguity of amplitude andestimate the non-stationary stereo noise signal n̂(f, τ ) =[n̂1 (f, τ ), n̂2 (f, τ )] as follows:n̂(f, τ ) =(n)(f, τ )W+
ICA (f )o(7)+

where M denotes the Moore-Penrose pseudo inverse matrixof M . It is obvious that this noise estimation is not perfect.
But it is still useful to enhance the NAM signal with nonlinear
noise reduction process using the noise spectral amplitude.

4972) Noise Reduction Process: In the noise reduction process,
SS using power spectrum of the estimated noise signals
is performed to effectively reduce the non-stationary noise
components. In the original BSSA, the delay-and-sum (DS)process is performed to combine the multi-channel target
and noise signals into monaural target and noise signals
before the SS. On the other hand, it is not straightforward
to combine the stereo NAM signal into the monaural NAM
signal with the DS process since the transfer function of
the NAM signal in each channel (i.e., each component ofa(f ) in Eq. (1)) is quite different from each other andhighly depends on not only the DOA but also various factors.
Therefore, in the proposed method, SS is performed in eachchannel. Moreover, to improve the noise estimation accuracy,
power spectrum of the estimated noise signal in each channel
is compensated with frequency-dependent and time-invariant
weights. These weights are determined so that the timeaveraged power spectrum of the estimated noise signal in anoise-only segment (e.g., a silence part at the beggining ofan utterance) is close to that of the detected mixed-signal
in the corresponding noise-only segment, which is regarded
as the target reference. The generalized SS (GSS) [15] isperformed to reduce the artiﬁcial distortion usually caused byan oversubtraction as much as possible. The enhanced stereo
NAM signal ŝ(f, τ ) = [ŝ1 (f, τ ), ŝ2 (f, τ )] extracted from thedetected stereo mixed-signal x(f, τ ) is given by⎧ 2ξ
⎨ |xc (f, τ )|2ξ − β|n̂c (f, τ )|2ξ ej arg(xc (f,τ ))ŝc (f, τ ) =, (8)(if |xc (f, τ )|2ξ > β|n̂c (f, τ )|2ξ )⎩0(otherwise)
where c is a channel index (c = 1, 2), β is an oversubtraction
parameter, and ξ is an exponential domain parameter.
The estimated stereo NAM signal includes the residual
noise components and is still artiﬁcially distorted even if
carefully tuning the GSS parameters. Consequently, there are
negligible acoustic differences between the enhanced stereo
NAM signal ŝ(f, τ ) and the original stereo NAM signals(f, τ ). To reduce the effect of these differences on NAM
recognition, noise superimposition process [16] is performed.
A pre-deﬁned stationary noise signal is superimposed on thedetected NAM signals with a constant SNR, and then the
acoustic model for NAM recognition is developed using themas the training data. In the recognition process, the same noise
signal is superimposed on the enhanced stereo NAM signal.
C. Frame-Wise Channel Selection Process
There are several choices to recognize the estimated stereo
NAM signal; e.g., a recognition process is performed in only
one pre-decided channel, or recognition processes are separately performed in individual channels and then a recognition
result in either channel is selected using some measures. Since
the noise signal in each channel varies differently from eachother, the channel causing larger distortion of the enhanced
NAM signal also changes frame by frame. Therefore, it
is expected that a frame-wise channel selection process iseffective.

Fig. 7. Difference of SNRs estimated frame by frame in individual channels(i.e., SNR1,τ − SNR2,τ in Eq. (9)).

As the selection measure to detect the enhanced NAM signal
less distorted by GSS, a SNR is estimated frame by frame
based on the detected stereo mixed-signal x(f, τ ) and theestimated stereo noise signal n̂(f, τ ) as follows:|xc (f, τ )|2 −|n̂c (f, τ )|2
SNRc,τ = 10 log10

f



f

|n̂c (f, τ )|2

. (9)

f

The channel with a higher estimated SNR is selected frameby frame. Figure 7 shows an example of the difference of
estimated SNR trajectories of individual channels. Based on
the selection result, the acoustic feature sequences of theenhanced NAM signals in individual channels are combined
into the single acoustic feature sequence.
There are other choices as selection measures, such as aresidual SNR using the noise suprressed signal, but a resultof our preliminary experiment showed that the SNR shown in
Eq. (9) yields the best recognition accuracy.
IV. E XPERIMENTAL E VALUATIONS
To investigate how much recognition accuracy is degradedby the speaker’s movements and evaluate the effectivenessof the proposed method, large vocabulary continuous NAM
recognition experiments were conducted.

We adopted 12 MFCCs, 12 ΔMFCCs and Δ power as
the acoustic features. The DFT size, window size, and shift
size were set to 1024, 512, and 256, respectively. Left-toright 3 state triphone HMMs with no skip were used as anacoustic model. The number of shared states was 2189 andthe state output probability distribution was modeled with
16 mixture components of GMMs. This acoustic model was
initially developed with normal speech database designed fortraining speaker-independent model, which included voicesof several hundreds of speakers. Then, it was adapted to
the NAM signals without non-stationary noise using iterative
MLLR. The number of adaptation sentences was 208 andthe number of test sentences was 143 sentences. They wereselected from Japanese newspaper articles. We simultaneously
used the NAM signals in both channels (i.e., 416 utterances intotal) to develop a common acoustic model for both channels.
We used 60 k word trigram language model trained withJapanese newspaper articles.
The following ﬁve settings were evaluated:
Unprocessed : The mixed-signals without any noise suppression processes were directly used.
• GSS : GSS using the time-averaged noise spectrum in thenoise-only segment was performed in each channel.
• Proposed BSSA : The proposed method using only BSSA
was performed.
• Proposed BSSA and selection : The proposed method
using both BSSA and frame-wise channel selection was
performed.
• Clean : NAM signal without suffering from nonstationary noise was used.
•

In the methods using GSS (GSS, Proposed BSSA, and Proposed BSSA and selection), a SNR of the superimposed noise
mentioned in Section III-B2 was set to 30 dB in both model
adaptation process and recognition process. The oversubtraction parameter and the exponential domain parameter in Eq.
(8) were set to 0.1 and 1/3, respectively. These settings wereexperimentally determined so that the recognition performance
was maximized.

A. Experimental Conditions

B. Experimental result

Stereo NAM data from a single Japanese male speaker
were recorded with two NAM microphones. The sampling
frequency was set to 16 kHz. To check how reasonable theassumption in the mixing process given by Eq. (3) was,
we conducted two types of experimental evaluations. In one
evaluation, we used simulated mixed-signals generated bysuperimposing the non-stationary noise signals, which weredetected when the speaker moved without speaking in NAM,
on the NAM signals, which were detected when the speaker
did not move. In the other evaluation, we used real mixedsignals detected in a real situation where the speaker moved
while speaking in NAM. In generating the simulated mixedsignals, power of the non-stationary noise signals was adjusted
so that the recognition performance for the simulated mixedsignal was nearly equal to that for the real mixed-signal.

Figure 8 shows the result for the simulated mixed-signals.
The non-stationary noise causes substantial degradation inword accuracy from 69.2% to 53.6% in the ﬁrst channel(ch1) and from 67.3% to 52.1% in the second channel (ch2).
The improvement in word accuracy by the conventional noise
reduction with a monaural signal (GSS) is observed but it islimited (55.5% in ch1 and 52.9% in ch2) since this process
cannot suppress the non-stationary noise components. Proposed BSSA yields signiﬁcantly larger improvements in word
accuracy (61.4% in ch1 and 61.6% in ch2) compared with GSS
since the non-stationary noise estimation is performed by thestereo signal processing. Moreover, frame-wise channel selection yields further improvement (63.3% of Proposed BSSA andselection). There still remain the noticeable difference in word
accuracy between Proposed BSSA and selection and Clean

498

viate the word accuracy degradation caused by non-stationary
noise generated by speaker’s movements during speaking.
Using a stereo signal detected with two NAM microphones,
the non-stationary noise was estimated with blind source
separation and independent component analysis, and spectral
subtraction was performed to enhance the stereo NAM signal.
Moreover, frame-wise channel selection was performed to construct less distorted monaural NAM signal. The experimental
results demonstrated that the proposed method is capable ofsigniﬁcantly reducing the degradation in NAM recognition
accuracy caused by the speaker’s movements.
Fig. 8.

Acknowledgment: This research was supported in part by
MEXT Grant-in-Aid for Scientiﬁc Research (A).

Result for simulated mixed-signals.

R EFERENCES

Fig. 9.

Result for real mixed-signals.

because the diffuse noise components are essentially difﬁcult
to be estimated.
Figure 9 shows the result for the real mixed-signals. Compared with the result for the simulated mixed-signals, word
accuracy in Proposed BSSA signiﬁcantly decreases (57.7% in
ch1 and 55.6% in ch2) and becomes close to that in GSS(56.7% in ch1 and 54.6% in ch2). We have noticed that a
directivity pattern shaped by the beam former to suppress the
NAM signal, which is given by a part of the unmixing matrix,
has the deep null at around zero degree of the DOA in thesimulated mixed-signals but the null becomes much shallower
and its degree is slipped in the real mixed-signals. This isbecause the assumption in the mixing process given in Eq. (3)is not valid in a real situation; i.e., the speaker’s movements
also cause the change of the transfer function a(f ). Consequently, the estimation accuracy of the non-stationary noise
becomes low. Nevertheless, the estimated stereo noise signal
is still useful to perform the frame-wise channel selection thatyields the best word accuracy (58.6%), which is statisticallysigniﬁcantly better than the results of Proposed BSSA and GSS
in the second channel. In a real situation, we don’t know whichchannel yields better word accuracy and it will vary dependingon the setting conditions of the NAM microphones. Therefore,
Proposed BSSA and selection capable of blindly selecting abetter channel is more effective than Proposed BSSA and GSS.
V. C ONCLUSIONSIn this paper, we proposed the blind noise suppression
method for Non-Audible Murmur (NAM) recognition to alle-499[1] B. Denby, T. Schultz, K. Honda, T. Hueber, J.M. Gilbert, and J.S.
Brumberg. Silent speech interfaces. Speech Communication, Vol. 52,
No. 4, pp. 270–287, 2010.
[2] S.-C. Jou, T. Schultz, and A. Waibel. Adaptation for soft whisper
recognition using a throat microphone. Proc. INTERSPEECH, pp. 1493–
1496, Jeju Island, Korea, Sep. 2004.
[3] T. Schultz and M. Wand. Modeling coarticulation in EMG-based
continuous speech recognition. Speech Communication, Vol. 52, No. 4,
pp. 341–353, 2010.
[4] T. Hueber, E.-L. Benaroya, G. Chollet, B. Denby, G. Dreyfus, and M.
Stone. Development of a silent speech interface driven by ultrasound andoptical images of the tongue and lips. Speech Communication, Vol. 52,
No. 4, pp. 288–300, 2010.
[5] A. Subramanya, Z. Zhang, Z. Liu, and A. Acero. Multisensory processing
for speech enhancement and magnitude-normalized spectra for speechmodeling. Speech Communication, Vol. 50, No. 3, pp. 228–243, 2008.
[6] Y. Nakajima, H. Kashioka, N. Cambell, and K. Shikano. Non-Audible
Murmur (NAM) recognition. IEICE Trans. Information and Systems, Vol.
E89-D, No. 1, pp. 1–8, 2006.
[7] T. Toda, K. Nakamura, T. Nagai, T. Kaino, Y. Nakajima, and K.
Shikano. Technologies for processing body-conducted speech detectedwith non-audible murmur microphone. Proc. INTERSPEECH, pp. 632–
635, Brighton, UK, Sep. 2009.
[8] P. Heracleous, V.-A. Tran, T. Nagai, and K. Shikano. Analysis andrecognition of NAM speech using HMM distances and visual information.
IEEE Trans. Audio, Speech, and Language Processing, Vol. 18, No. 6,
pp. 1528–1538, 2010.
[9] D. Babani, T. Toda, H. Saruwatari, K. Shikano. Acoustic model training
for non-audible murmur recognition using transformed normal speechdata. Proc. ICASSP, pp. 5224–5227, Prague, Czech Republic, May. 2011.
[10] Y. Takahashi, T. Takatani, K. Osako, H. Saruwatari, and K. Shikano.
Blind spatial subtraction array for speech enhancement in noisy environment. IEEE Trans. on Audio, Speech and Language Processing, Vol. 17,
No. 4, pp. 650–664, 2009.
[11] S. Boll. Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol. 27,
No. 2, pp. 113–120, 1979.
[12] M.J.F. Gales. Maximum likelihood linear transformations for HMMbased speech recognition. Computer Speech and Language, Vol. 12, No.
2, pp. 75–98, 1998.
[13] H. Saruwatari, S. Kurita, K. Takeda, F. Itakura, T. Nishikawa, and K.
Shikano. Blind source separation combining independent component
analysis and beamforming. EURASIP Journal on Applied Signal Processing, Vol. 2003, No. 11, pp. 1135–1146, 2003.
[14] S. Ikeda and N. Murata. A method of ICA in time-frequency domain.
Proc. ICA, pp. 365–370, Aussions, France, Jan. 1999.
[15] B.L. Sim, Y.C. Tong, J.S. Chang, and C.T. Tan. A parametric formulationof the generalized spectral subtraction method. IEEE Trans. on Speech
and Audio Processing, Vol. 6, No. 4, pp. 328–337, 1998.
[16] S. Yamade, A. Lee, H. Saruwatari, and K. Shikano. Unsupervised
speaker adaptation based on HMM sufﬁcient statistics in various noisy
environments. Proc. INTERSPEECH, pp. 1493–1496, Geneva, Switzerland, Sep. 2003.