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Title

An LP‑based blind model for restoring bone‑
conducted speech

Author(s)Vu, Thang tat; Unoki, Masashi; Akagi, Masato

Citation

Second International Conference on Communications
and Electronics, 2008 (ICCE 2008): 212‑217

Issue Date

2008‑06

Type

Conference Paper

Text version

publisher

URL

http://hdl.handle.net/10119/9955

RightsCopyright © 2008 IEEE. Reprinted from Second
International Conference on Communications andElectronics, 2008 (ICCE 2008), 2008, 212‑217.
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Description

Japan Advanced Institute of Science and Technology

An LP-based blind Model for Restoring
Bone-conducted Speech
Thang tat Vu, Masashi Unoki, and Masato Akagi
School of Information Science, Japan Advanced Institute of Science and Technology
1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan
Email: {vu-thang, unoki, akagi}@jaist.ac.jp
Abstract—Due to the stability against the external noise, boneconducted (BC) speech seems better to be used instead of noisy
air-conducted speech in an extremely noisy environment.
However the quality of bone-conducted speech is very low andrestoring bone-conducted speech is a challenged topic in speech
signal processing field. As the main issue to improve the BC
speech, many studies try to model and resolve the degradation
when the signal is conducted through bone transduction. In
previous study, we proposed a linear prediction (LP) based blindrestoration model. In this paper, we therefore completely
evaluated the proposed model in comparison with other models
to find out whether our proposed model could adequately
improve voice quality and the intelligibility of BC speech, using
objective measures (LSD, MCD, and LCD) and carrying out
Japanese word-intelligibility tests (JWITs), Vietnamese wordintelligibility tests (VWITs) and Modified Rhyme Tests (MRTs)
for English. The results of experiments on different languages,
i.e. Japanese, English and Vietnamese proved the practicabilityof blind-BC restoration.
Key words- Speech intelligibility, Bone-conducted (BC) speech,
Simple recurrent network (SRN), Blind restoration.

I. INTRODUCTION
It is required for safe and secure speech communication
while the interfering noises in extremely noisy environments
bring significant difficulties for the commutations of humanand also automatic speech recognition (ASR) systems. This
problem is from the low sound-quality and low intelligibility ofspeech, due to the influence of the transmission environment.
As a solution, many different complex models and algorithms
have been used to cancel or reduce these noisy affections but
have been only efficient at low- and medium-noise levels.
When the noise levels are extremely high, this solution seems
to perform ineffectively.
Another possible solution is to use a special microphone torecord the speech signals transmitted through the speaker's
head and face [1-5]. This recorded signal is referred to as“bone-conducted (BC) speech”. Its stability against interfering
noise from noisy environments makes BC speech moreadvantageous than noisy air-conducted (AC) speech.
There are two main drawbacks of BC speech, thedegradation due to bone-conduction and the changing of
speaker’s pronunciations due to surrounding noise referred as
Lombard effect. While the Lombard effect is a usual problem
as the same as AC speech in noisy environments, the other is

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critical affection to the speech quality. When the signal istransmitted through bone-conduction, it is complexly affected
at a loss of sound quality and speech intelligibility. The
degradation varies for different pick-up points (BC microphone
positions), speakers, and pronounced syllables. This is because
the characteristics of bone-conduction vary for different
measuring positions and the distribution of frequency
components varies with speakers who pronounce syllables
differently. Regarding this main issue, we show a possibility ofa blind restoration method for restoring quality andintelligibility of BC speech.
There are several studies on BC speech for applications
such as human-hearing aids and machine-hearing systems but
the results are still limited. A Gaussian mixture model (GMM)based voice conversion model was applied to restore bodytransmitted speech, which is like BC speech [6]. Due to thedifficulty of dealing with F0 features that might cause synthesis
problems, this approach has only been applied to unvoiced
speech such as whispered speech. In other approaches such as
when air-and-bone conductive micro-phones are used, BC
speech has been used for ASR systems [7]. However, it has justbeen an additional source and helps to reduce external noisefrom noisy AC speech.
Our approach here is to improve BC speech signal andapply directly the restored signals in speech applications innoisy environments with greater efficiency instead of using
noisy AC speech. This is even more extremely challenging toblindly restore the signals of BC speech without using anyother information of AC speech.
Information on AC speech is usually needed to construct
the inverse filtering in restoration models [1-3] and this is aserious drawback in practice when we have no information on
AC speech. To construct its associated inverse filtering, thecross-spectrum and long-/short-term Fourier transform
methods [1, 2] depends on the AC spectrum, the MTF-basedmodel depends on the gain of power envelope of AC speech
and the LP-based model depends on AC-LP coefficients [3].
Averaged gains or averaged AC-LP coefficients can be chosen
for constructing an averaged filtering but the models are
difficult to adapt to BC speech signal.
Since LP-based model only depends on a few unknown LP
coefficients of AC speech (AC-LP coefficients), we proposed ablind restoration model [4], a machine learning method wasapplied to predict various LP coefficients of AC speech signal
from LP coefficients of BC speech signal. The drawback of212

TABLE I.

Figure 1. Environment for recording AC/BC speech.

this model was that the LP coefficients were not suitable toenable prediction with statistical models due to the different
roles played by LP coefficients and their relatively large
dynamic ranges. Besides, the inverse filtering of this model
was not adapted to the short-term changing of BC speech
signal. However, although the model suffered from above
limitations, we obtained reasonable results. These results
revealed the existing useful relationship between the AC-LP
and BC-LP coefficients for restoring BC speech.
Therefore, we improved the previous LP-based model by(1) extending long-term to frame-based processing, (2) using
LSF coefficients on LP representations, and (3) predicting LSF
parameters on a frame-by-frame basis via a recurrent neuralnetwork. Since LSF coefficients play the same role in thepresentation of the spectrum envelope and their values arelimited within a range (0, π), these coefficients could help toalleviate the limitations with LP coefficients in prediction using
statistical methods. The processes of restoration on a frame-byframe basis could also be adapted to inverse filtering in realtime. In addition, since the restoration of neighboring framesshould be related, a recurrent network was applied to predict
BC-LSF coefficients to complete the blind restoration system.
In this paper, we completely evaluated the proposed modelin comparison with other models to find out whether our
proposed model could adequately improve voice quality andthe intelligibility of BC speech, using objective measures(LSD, MCD, and LCD) and carrying out Japanese wordintelligibility tests (JWITs), Vietnamese word-intelligibility(VWIT) and Modified Rhyme Tests (MRTs) for English. The
results of experiments on different language, i.e. Japanese,
English and Vietnamese proved the practicability of blind-BC
restoration.
The rest of this paper is organized as follows. the next
section briefly describes AC/BC speech databases that weconstructed for English, Japanese and Vietnamese languages.
In section 3, we describe the restoration modes based on LP
methods and in details about our proposed LP-based model(LSF model). The incorporating of a simple recurrent network(SRN) into the LSF model brings us the ability of blind
restoration for BC speech. In section 4, we completely evaluate
the proposed models in comparison with other models. Bothobjective and subjective measures are carried out for evaluating
the restoration ability of models on different BC databases.

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LIST OF EQUIPMENTS

Measurement site
Number of Pickup points
Number of speakers
RecorderFormatSample rate
Number of channels
Mic. A for AC speechMic. power supply A
Mic. B for BC speechMic. C for BC speechMic. amp. B and C

Soundproof room5
10 (Jp), 6 (En), 10(Vie)MARANZ, PMD671
16 bits PCM
48 kHz2 (Left:AC, Right: BC)
SONY, C536P
SONY, AC148F
TEMCO, HG-17
TEMCO, SK1
Handmade

Finally, section 6 concludes with a summary and mentions
future work.
II. AC/BC SPEECH DATABASE
We assumed that there were existing relationships between
AC and BC speech that were significant in restoring BC
speech. Therefore, a database was essential for analyzing therelationships and differences between BC speech and clean AC
speech signals before any models were used to restore BC
speech. We constructed large-scale databases of English,
Japanese and Vietnamese, containing pairs of BC and clean AC
speech signals recorded simultaneously.
Figure 1 and Table I show the environment and equipments
used to construct databases. The BC speech was collected atfive different pick-up points on the head and face: the (1)mandibular angle, (2) temple, (3) philtrum, (4) forehead, and(5) calvaria. Microphone B was used at the pick-up point (5)and microphone C was used at the others.
Six speakers (three males, three females) participated in therecording of 300 English words in the MRT list.
In the Japanese database, ten speakers (five males and fivefemales) participated in the recording of 100 words and all 101
syllables. The 100 Japanese words were chosen by the degreeof familiarity in NTT-AT 2003 database [3], 25 words for eachfamiliarity range: R1 (1.0,2.5) - low, R2 (2.5,4.0) - medium
low, R3 (4.0,5.5) - medium high, and R4 (5.5,7.0) - high. It is
known that there is a complementary relationship between
familiarity and intelligibility [5]. The selection of words in
different familiarity ranges would help us to carry out better
JWIT.
In the Vietnamese database, ten speakers (five males andfive females) participated in the recording of 100 words whichwere chosen by the frequencies indexes and whether the word
type is mono-syllabic or duo-syllabic. There are 30 monosyllable words for each level of frequencies indexes: C1(mono, low), C2 (mono, high), and also 20 duo-syllabic words
for each of two level: C3 (duo, low), C4 (duo, high). It is
known that there is a complementary relationship between
frequency index and intelligibility [3]. Beside, it is usuallymore difficult to understand a mono-syllabic word than a duosyllabic word even for a Vietnamese. The selection of words in
different frequency ranges would help us carry out better
VWITs.

213

Figure 2. Block diagram of proposed model - LSF-SRN

III.

RESTORATION APPROACHESP

A( z ) = ∑ a (i ) z − i ,A. LP Representation
Let x(n) and y(n) be AC and its associated BC speech. The
signals are represented by LP model in the z-domain as:P−Gx ( z ) = X ( z ) ∑ ax (i ) z ,−iax (0) = −1 ,(1)−G y ( z ) = Y ( z ) ∑ a y ( i ) z − i ,a y (0) = −1 ,

(2)

i =0
Q

a (0) = 1

(5)

U ( z ) = A( z ) + z − ( P +1) A ( z − 1 ),

(6)

i=0

V ( z ) = A( z ) − z− ( P + 1)

Since the LP residues are related to the source information(glottal information), we can approximately assume the residue
ratio as a constant factor, k, as [3]
G y ( z ) Gx ( z ) = k(const.) .

(3)

-1

The inverse filter H (z) to restore BC to AC speech is derived
from Eqs. (1) – (3), simply as
Q−i∑ a y (i ) z
X ( z)
−1
i =0
=k⋅ P
H ( z) =.
−i
Y ( z)∑ a (i ) z
i =0

(4)

x

The inverse filtering H-1(z) can be decomposed into twoparts. In the first part, the constant value, k, can be chosen
manually and used to control the magnitude of restored speech.
The second part primarily depends on the LP coefficients ofsignals. Although unknown AC-LP coefficients could be
predicted from BC-LP coefficients with some reasonable
results [4], they were inappropriate for statistical models in
prediction because they played different roles and had arelatively wide dynamic range. LSF coefficients should be abetter choice. They have both a well-behaved dynamic range
and can be used to encode LP spectral information more
efficiently than any other parameters [5]. The almost equivalentroles of LSF coefficients, on the other hand, would be suitable
for statistical models.
B. LSF Representation
Let A(z) be a general LP filter on an LP representation. The
LSF coefficients, ωi and θi , can be derived from a symmetricpolynomial U(z) and an anti-symmetric polynomial V(z), as thephase of conjugated zeros.

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A( z ) .

(7)
Substituting Eqs. (5)-(7) into Eq. (4), we can obtain theequation for the inverse filtering which depends on the LSF
coefficients instead of the LP coefficients

i =0

where X(z) and Y(z) are the z-transforms of x(n) and y(n), P and
Q are LP orders, and ax(i) and ay(i) are i-th LP coefficients.
Here, Gx(z) and Gy(z) are the z-transforms of the LP residues
gx(n) and gy(n).

−1

H −1 ( z ) = k ⋅U y ( z ) + Vy ( z )(8)U x ( z ) + Vx ( z )C. Blind BC restoration model
Figure 2 shows a block diagram of the LP-based blind
restoration model. In this section, we explain how to predict
AC-LSF coefficients from BC-LSF coefficients.
The problem: Let VY be the observed vector of BC-LSF
coefficients VY (ly(1), ly(2), ... , ly(q)), and let VX be theassociated vector of AC-LSF coefficients VX (lx(1), lx(2), ... ,lx(p)). We need to predict approximately the best match seriesof output vector VX from a series of input vector VY. As thecharacteristic of LSF coefficients, the LSF coefficients invectors VX and VY have to agree with the following equations:0 < lx(1) < lx(2) < ... < lx(p) < π ,0 < ly(1) < ly(2) < ... < ly(q) < π .

(9)
(10)
LSF differentials have positive values in the range of (0, π).
Using LSF differentials can help simplify the problem. Let VY’
be the observed vector of BC-LSF differences VY’ (δy(1),δy(2),..., δy(Q)) where δy(1)=l(1), δy(i) = ly(i) - ly(i-1), 2≤ i≤Q,
and let VX’ be the predicted vector of AC-LSF
differential VX’(δx(1), δx(2),..., δx(P))whereδx(1)=lx(1),δx(i)=lx(i) - lx(i-1), 2≤ i≤P. We need to obtain a prediction
model M that approximate the best match series of output
vector VX’ from a series of input vector VY’ as: {VX’}
M({VY’}).
Since the overlap between every two neighbors in the seriesof speech frames, their restorations should be related. We
propose the application of an Elman – a simple recurrent
network (SRN) － to the prediction problem. The function
learnt by this network depends on not only the current inputs
but also previous states of the network and this should be agood choice for automatically predicting AC-LSF coefficients.

214

In this paper, we chose k = 1 and set both LP-orders as P =Q = 20. There were 20 nodes for each layer: input layer, output
layer and hidden layer. The length of frames is 250 ms, and theoverlap of two neighbors is 125 ms. These values are to keep
the frame-length short enough, but also reduce the number oftraining vectors for a small prediction model.
IV. EVALUATION
The aim of this evaluation was to investigate whether the
proposed models could adequately restore BC speech to attainbetter voice-quality and speech intelligibility and whether this
could work well blindly. Using both objective and subjective
measurements, we evaluated a previous long-term Fouriertransform model [2] and the two proposed LP-based models(one is non-blind and the other is blind model). Then, there
were two un-blind models: (1) LTF: the long-term Fouriertransform and (2) LSF: LP-based models using LSF coefficientsand frame basis processing. The proposed blind restoration
model were (3) LSF-SRN: LP-based blind restoration - apply
SRN to LSF.
A. Objective measurements
We used LSD (log-spectrum distortion), LCD (LP distance),and MCD (MFCC distance) to evaluate methods. These threeobjective measurements were computed as follows:2W 20log S (ω ) Sˆ(ω)  ,
1 ∑
10

W ω 
P
2
LCD = 1 ⋅ ∑ a x (i) − a y (i) ,P i =1

)

(

LSD =

(

(12)

)

12

MCD = ∑ (cx,i − c y,i )2 .
i =0

(13)
(14)

where W is the upper frequency (8 kHz in this case), theamplitude spectra getting by 1024-points FFT calculation of 25ms frames, 15-ms overlapping. ax(i) and ay(i) are the i-th LP
coefficients of signals with the LP order being set P=20, and cx,iand cy,i are the i-th MFCC of the signals. Table II shows the
distances between clean AC speech signal and the signals (theobserved BC speech and the restored speech signals). In
general, the LSF model is the best model for every objective
measurement. LSF-SRN is the following model even it blindly
restores BC speech.
B. Subjective evaluation
The modified rhyme tests (MRTs) were carried out on
English database with six subjects, the JWITs were carried out
with forty Japanese subjects, and the VWITs were carried out
with fifteen Vietnamese subjects. All the selected subjects havenormal hearing.
1) Modified rhyme test (MRT)The MRTs are carried out on English database. Listeners
are shown six-word lists and then asked to identify which of
the six is spoken. There are 50 six-word lists of rhyming or
similar sounding mono-syllabic English words. Every word isin C-V-C sound sequence, and the six words in each list differ
only in the initial or final consonant sound. The result by MRT

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TABLE II.

OBJECTIVE DISTANCES TO AC SPEECH SIGNALS

Objective
Language Measure
LSD
MCD
EnglishLCD
LSD
MCD
Japanese
LCD
LSD
Vietnamese MCD
LCD
TABLE III.

BC

Non-blind
LTF
LSF

14.28
21.72
3.04
12.08
20.52
2.79
11.91
26.93
2.22

13.57
18.15
2.91
11.33
19.37
2.51
11.50
17.24
1.86

Blind
LSF-SRN
9.64
15.96
2.43
11.21
19.39
2.58
11.37
20.45
1. 84

8.92
12.55
1.79
10.38
17.53
1.83
9.57
14.24
1.28

MODIFIED RHYME TEST, CORRECTION (%).

BC

LTF

LSF

LSF-SRN

AC

69.7

76.3

88.0

82.5

95.7

indicate errors in discrimination of both initial and final
consonant sounds, and also show the improvement in
intelligibility of restored speech [8]. Table III shows the correctscore of MRT. As the same as objective measurements, theLSF model gain the best result, following is the LSF-SRN.
2) Japanese word-intelligibility test (JWIT)
In JWITs, the speech signals of eighty Japanese words wereplayed in random order. The Japanese subjects, who did notknow these words previously, were requested to listen eachword only one time and write down what they got in hiragana.
We intend to evaluate the word intelligibility of these signals
on 4 familiarity ranges. Since each subject should listen to aword only one time, we divide 40 subjects into five listening
group A, B, C, D and F to listen 400 stimuli. Table IV shows
us the way to arrange 400 stimuli for five listening group. In
generally, the intelligibility could be evaluated by the average
recognition accuracy which is scored by all subjects. Table VI
lists the recognition accuracy scores of JWITs.
3) Vietnamese word-intelligibility test (VWIT)
In VWITs, the speech signals of forty words were played in
random order. The Vietnamese subjects, were requested tolisten each word only one time and write down what they got.
We intend to evaluate the word intelligibility of these signals on
4 different categories C1-C4, with different types of words(mono or duo syllabic) and levels of frequency indexes (low orhigh frequency index). As the same as in JWITs, we divide 15
subjects into five listening groups A’, B’, C’, D’ and F’ to listen
200 stimuli. Table V shows us the way to arrange 200 stimuli
for five listening groups. Table VII lists the score results of
VWITs.
C. Discussion
By evaluation results in Table II, III, VI and VII, the non-blind
LP-based model LSF and the blind LP-based model LSF-SRN showed
the significant ability to restore BC speech, both intelligibility (LSD,
MRT, JWIT and VWIT) and spectral distance (LCD, MCD).

As JWIT scores, LSF model improved 36.5% of average
recognition accuracy, LSF-SRN model follows with an
expressed result 20% greater. It is the same result with VWIT,
whereas LTF model improved 30% of average recognition

215

TABLE IV.

JAPANESE WORD INTELLIGIBILITY STIMULI

Word Index

BC

1–4
5–8
9–12
13–16
17–20
21–24
25–28
29–32
33–36
37–40
41–44
45–48
49–52
53–56
57–60
61–64
65–68
69–72
73–76
77–80

R1 (1.0–2.5)
Lowfamiliarity
R2 (2.5–4.5)
Medium Low
familiarity

R3 (4.5–5.5)
Medium Highfamiliarity

R4 (5.5–7.0)
Highfamiliarity

TABLE V.

C2
Mono syl.
High freq.

C3
Duo syl.
Low freq.

C4
Duo syl.
High freq.

E
D
C
B
A
E
D
C
B
A
E
D
C
B
A
E
D
C
B
A

LTF LSF

A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’

B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’

C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’

LSF
SRN
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’

AC
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’
E’
D’
C’
B’
A’

RESULTS OF JAPANESE -WORD INTELLIGIBILITY TESTS (%)

Familiarity
R1 (1.0–2.5)
R2 (2.5–4.5)
R2 (4.5–5.5)
R4 (5.5–7.0)
Avg (1.0-7.0)
TABLE VII.

BC

1–2
3–4
5–6
7–8
9–10
11–12
13–14
15–16
17–18
19–20
21–22
23–24
25–26
27–28
29–30
31–32
33–34
35–36
37–38
39–40

C1
Mono syl.
Low freq.

AC

VIETNAMESE WORD INTELLIGIBILITY STIMULI

Word Index

TABLE VI.

A
E
D
C
B
A
E
D
C
B
A
E
D
C
B
A
E
D
C
B

LSF
LTF LSF SRN
B
C
D
A
B
C
E
A
B
D
E
A
C
D
E
B
C
D
A
B
C
E
A
B
D
E
A
C
D
E
B
C
D
A
B
C
E
A
B
D
E
A
C
D
E
B
C
D
A
B
C
E
A
B
D
E
A
C
D
E

BC
3.5
3.0
13.0
20.5
10.0

LTF
3.5
3.0
21.0
36.0
15.9

LSF
26.0
37.0
58.0
64.5
46.4

LSF-SRN
14.5
19.0
43.0
43.5
30.0

AC
66.0
63.0
71.5
77.5
69.5

RESULTS OF VIETNAMESE-WORD INTELLIGIBILITY TESTS (%)

Category

BC

LTF

LSF

C1 (mono, low)
C2 (mono, high)C3 (duo, low)
C4 (duo, high)
Avg

43.3
60.0
60.0
50.0
53.3

63.3
70.0
53.3
40.0
56.7

73.3
90.0
90.0
70.0
80.8

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LSF
SRN
50.0
80.0
70.0
56.7
64.2

accuracy and, LSF-SRN model follows with a result 11%
greater. In general, we found that, it is more difficult to restore
BC speech at low familiarity. At ranges R1 and R2, LTF modeleven gain no improvement. The improvement result increases
quickly with the higher familiarity. LSF model even improve
the average recognition accuracy with 40% greater in highfamiliarity ranges R3 and R4. At these familiarity ranges, LSFSRN improved the BC speech up to almost the same scores
43% as LSF. We also found that, Even it is a blind model,
LSF-SRN showed expressed ability to improve the voice
quality and intelligibility of BC speech signal. The
intelligibility improvement seems to be independent oflanguages with expressed results for English (MRTs), Japanese(JWITs) and also Vietnamese (VWITs). It means the SRN wastrained adequately for predicting AC-LSF coefficients and help
LSF-SRN model achieve good restoration.
V. CONCLUSION
We improved the LP-based model by (1) extending longterm to frame-based processing, (2) using LSF coefficients on
LP representations, and (3) predicting LSF parameters on aframe basis via an Elman network. We entirely evaluated thedeveloped model in comparison with previous models to findout whether the developed model can adequately improvevoice quality and the intelligibility of BC speech, using threeobjective measures and three subjective tests.
The results of experiments showed that the LP-based model
proved the significant practicability of blind-BC restoration.
Especially, the model can be applied to improve theintelligibility of BC speech on different languages.
The next challenge is to improve the spectral distances
since the blind restoration model we proposed is still limited inthis regard. Significant improvements in both intelligibility andspectral distances remain problems that need to be solved toconstruct a blind restoration model that will be as good as the
LSF model. The factor k is currently assumed as constant in all
LP-based models and can be even further improved byconsidering its change. We should consider the problem ofLombard effect in future work. The same methodology
concepts of LP-based models can be applied. Noisy BC speech
can be regarded as original BC speech but the SRN of blind
LP-based models should be re-trained.
ACKNOWLEDGMENTS
This work was supported by the YAZAKI Memorial
Foundation for Science and Technology and a scheme for the``21st Century COE Program'' in Special Coordination Funds
for the Promotion of Science and Technology made available
by the Ministry of Education, Culture, Sports, Science, andTechnology in Japan. This was also partially supported by aGrant Program by the SCOPE (071705001) of Ministry ofInternal Affairs and Communications (MIC), Japan.

AC
76.7
90.0
100.0
100.0
91.7

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