୍⯡♫ᅋἲே 㟁Ꮚ᝟ሗ㏻ಙᏛ఍                      ಙᏛᢏሗ‫ٻ‬‫ٻڇڮڞڤکڪڭگڞڠڧڠٻڡڪٻڠگڰگڤگڮکڤٻڠڣگ‬‫ٻۏۍۊۋۀڭٻۇڼھۄۉۃھۀگٻڠڞڤڠڤ‬THE INSTITUTE OF ELECTRONICS,                      TECHNICALREPORT OF IEICE.
INFORMATION AND COMMUNICATION ENGINEERS‫ٻڮڭڠڠکڤڢکڠٻکڪڤگڜڞڤکڰڨڨڪڞٻڟکڜٻکڪڤگڜڨڭڪڡکڤ‬                ‫ٻڄڍڌڈڏڌڋڍڃڌڌڌڈڏڌڋڍګڮ‬‫ٻ‬

Articulatory Controllable Speech Modiﬁcation using Sequential Inversion
and Production Mapping with Gaussian Mixture Models
Patrick LUMBAN TOBING† , Tomoki TODA† , Graham NEUBIG† , Sakriani SAKTI† , Satoshi
NAKAMURA† , and Ayu PURWARIANTI††† Graduate School of Information Science, Nara Institute of Science and Technology
Takayama-cho 8916-5, Ikoma, Nara, 630–0192 Japan†† School of Electrical Engineering and Informatics, Institut Teknologi Bandung
Jl. Ganesha 10, Bandung 40132 Jawa Barat - Indonesia
Abstract In this report, we propose an articulatory controllable speech modiﬁcation framework using statistical
inversion and production mapping with Gaussian Mixture Models. The proposed framework enables us to modify
speech waveforms by manipulating unobserved articulatory parameters estimated in the inversion mapping and generating the modiﬁed speech waveforms from the manipulated articulatory parameters in the production mapping.
We also propose an articulatory manipulation method that considers inter-dimensional correlation between articulators. The experimental results show that the proposed framework is capable of successfully modifying phoneme
sounds by manually controlling related articulators.
Key words speech modiﬁcation, articulatory control, inversion mapping, production mapping, Gaussian Mixture
Modelsto estimate acoustic parameters from articulatory parame-

1. Introduction

ters input.

During human speech production, articulators are mov-

Earlier, those acoustic and articulatory mapping systems

ing together in a proper way so that a certain speech soundwere approached by mathematical production models [6], [7].

can be produced with particular resonance characteristics ofHowever, speech production process is too complex to be

the vocal tract. Hence, speech sound is able to be charac-

mathematically modeled without some approximations. Re-

terized not only by its acoustic properties, but also by ar-cently, with the emergence of parallel acoustic-articulatory

ticulators properties, such as articulatory movements duringdata, it is possible to estimate the speech production pro-the speech production. In terms of modifying speech, it iscess with statistical approaches, instead of mathematicalof course a more intuitive way to manipulate the articula-modelling. There have been proposed several works in the

tory parameters rather than the acoustic parameters [1], [2].

category of statistical methods, e.g., mapping systems us-

In addition, with its slower varying trajectories compared toing codebooks [8], [9], hidden Markov models (HMMs) [10],acoustic parameters [3], articulatory parameters are promis-[11], neural networks [12], [13], and Gaussian Mixture Mod-ing to become a better parameterization method in a varietyels (GMMs) [1], with the eﬀectivity of both inversion andof applications, such as speech recognition [4], speech synthe-

production mapping processes reported in each respective

sis [5], and speech coding [6].

method. In addition, it is also reported that by manipu-

In order to utilize the articulatory parameters in a speech

lating certain articulatory movements, a particular phoneme

modiﬁcation system, it is inevitable to ﬁrstly develop map-

sound can be eﬀectively modiﬁed in an articulatory control-ping models between speech acoustic and articulatory pa-

lable HMM-based text-to-speech synthesis [2].

rameters. Typically, there are two distinct mapping systems that cover this relationship [1].

In this work, we propose a novel articulatory controllable

One is acoustic-to-

speech modiﬁcation system, inspired by the previous work

articulatory mapping process or so called inversion mapping.

for GMM-based inversion/ production mapping methods [1].

It is used to estimate articulatory parameters from acoustic

The proposed system is capable of sequentially estimating

parameters input. The other one is articulatory-to-acoustic

articulatory parameters from a given input speech signal

mapping process or so called production mapping. It is usedusing the GMM inversion mapping, manipulating the esti—1—

This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere.

Copyright ©2014 by IEICE

mated articulatory parameters, estimating acoustic parame-

mined by performing principal component analysis for theters from the manipulated articulatory parameters using thetraining data.

GMM production mapping, and ﬁnally synthesizing a modiﬁed speech signal from the estimated acoustic parameters.

joint static and dynamic feature of articulatory parameters, , where Δxt is the dywhich is given by X t = xt , Δxt

For the manipulation of the estimated articulatory parame-

namic feature vector of the articulatory parameters at frameters, we also develop a method capable of reﬁning unmodiﬁedt, which is calculated as Δxt = xt − xt−1 .

parts according to the modiﬁed parts of the estimated artic-

Whereas, the target feature consists of a2. 1. 2 Training processtion. The proposed system has a great potential to develop

In the training process, a joint source and target feature 
constructed at each frame is used as thevector O t , Xtvarious speech applications, such as speech recovery for vo-

training data. Then, the joint probability density function

cally disabled people, pronunciation enhancement in speak-of the source and target features in the inversion mapping is

ing foreign languages, and concealing messages by modifying

modeled with a GMM as follows:
P O t , X t |λ(O,X)

ulatory parameters by considering inter-dimensional correla-particular phoneme/sounds. Furthermore, this system is capable of easily applied to any language, as it only needs aspeech signal as input without the needs for text/language
speciﬁcation input like in [2].

=M
α(O,X)NmO
t , Xt; μ(O,X), Σ(O,X)
,
m
m

(2)

m=1

2. Inversion and Production Mappingwith GMM [1]where m denotes the mixture component index and the total number of mixture components is M . The normal distribution is denoted as N (; μ, Σ) with mean μ and covari-In this paper, a set of simultaneously recorded acoustic-

ance Σ.The parameter set of the GMM, which is denoted as(O,X)

articulatory data is used in the training process to developλ(O,X) , consists of mixture-component weights αmspeaker dependent GMMs as both inversion and production

and covariance matrices Σmvectors μ(O,X)
mmapping models. The data are provided in MOCHA [14],mixture components.

(O,X), meanfor individual

where 14-dimensional electromagnetic articulograph (EMA)

2. 1. 3 Conversion processdata is used as articulatory parameters.

They represent

In the conversion process, the target articulatory param-

movements of seven articulators (lower incisor, upper lip,

eter sequence x corresponding to the given input speech isbottom lip, tongue tip, tongue body, tongue dorsum, andestimated by maximizing the conditional probability density

velum) movements in x- and y- coordinates on the midsagit-

function P (X|O, λ) given the mel-cepstral segment featuretal plane.

sequence O. This conditional probability density function

Let assume ct , st , and xt as spectral envelope parameters

given by a GMM is eﬀectively approximated with a single(mel-cepstrum), source excitation parameters (log-scaled F0

Gaussian distribution using a single mixture component se-

and log-scaled power), and the articulatory parameters re-quence m = {m1 , · · · , mT }. The suboptimum mixture com-

spectively.

Time sequence vectors of individual parame ,ters over an utterance are denoted as c = c
1 , · · · , cT


 
 
 
 
s = s1 , · · · , sT , and x = x1 , · · · , xT , respectively,ponent sequence m̂ is determined ﬁrst by:m̂(O) = arg max P m|O, λ(O,X) .

where T is the number of frames and  denotes the trans-

After that, the estimated articulatory parameter sequence isposition of the vector.

m(3)

determined by:

2. 1 Inversion mapping
In the inversion mapping, the spectral envelope parameters extracted from an input speech signal are converted into
the corresponding articulatory parameters.

x̂ = arg max P X|O, m(O) , λ(O,X) ,(4)

subject to X = W (x) x,(5)m

2. 1. 1 Source and target features

where W (x) is a transformation matrix for creating the joint

The source feature consists of a framewise mel-cepstral seg-static and dynamic feature sequence vector X from the static

ment feature vector extracted from mel-cepstrum parameters

feature sequence vector x.

at multiple frames around the current frame. At frame t, the2. 2 Production mapping

mel-cepstral segment feature vector is denoted by O t , which

In the production mapping, both the articulatory param-

is deﬁned as




+ b,
O t = A ct−L , · · · , ct · · · , ct+L

eters and the excitation parameters are converted into the(1)where A and b are linear transformation parameters deter-

spectral envelope parameters.
2. 2. 1 Source and target features
The source feature consists of joint static and dynamic—2—

feature vectors of articulatory parameters and excitation parameters, which is given by Y t = [xt , Δxt , st , Δst ] at framet. Whereas, the target feature consists of a joint static anddynamic feature vector of mel-cepstrum C t = [ct , Δct ] atframe t.
2. 2. 2 Training process
The training process of the production mapping is similar to that of the inversion mapping described in Section

Figure 1 Proposed speech modiﬁcation process [18]2. 1. 2. At each frame, a joint source and target feature vec 
is constructed. Then, the joint probability
tor Y t , Ctmanipulated articulatory parameters and that of the source

density function of the source and target features is modeled

nal is generated from the converted mel-cepstrum parameterwith a GMM as follows:
P Y t , C t |λ(Y,C)

sequence ĉ and the source excitation parameter sequence s=Mα(Y,C)NmY
t , Ctexcitation parameters s. In the end, a modiﬁed speech sig-

by using vocoder.
In this framework, we often want to manipulate only the; μ(Y,C), Σ(Y,C)
m
m

,

(6)

m=1

movements of some speciﬁc articulators, such as a tongue tip
or velum, rather than to manipulate those of all articulators.

2. 2. 3 Conversion process

In this report, we implement two manipulation methods for

The conversion process of the production mapping is also

modifying the estimated articulatory parameters.

similar to that of the inversion mapping described in Section

3. 1 Simple manipulation method

2. 1. 3. First, for a given time sequence of the source feature

Consider the estimated D-dimensional articulatory param-

vectors Y , the suboptimum mixture component sequence m̂eters at frame t, x̂t = [x̂t (1), · · · , x̂t (D)] that is gener-

is determined by:ated from the inversion mapping. The manipulated artic-

m̂

(Y )



= arg max P m|Y , λ

(Y,C)

m



.

(7)

Then, the mel-cepstrum sequence vector ĉ is determined by:(8)ĉ = arg max P C|Y , m(Y ) , λ(Y,C) ,csubject to Y = W (y) y,ulatory parameters are denoted as x̂t . In order to modify particular articulatory movements, only corresponding
dimensions of articulatory parameters are modiﬁed.

Forexample, if the ﬁrst and second dimensions are modiﬁed,
then the manipulated articulatory parameters become x̂t =

(9)[x̂t (1), x̂t (2), · · · , x̂t (D)] .
It is well known that some articulatory movements arewhere W (y) is a transformation matrix for creating the joint

strongly correlated to each other [16]. As this simple ma-

static and dynamic feature vector sequence Y from the static

nipulation method ignores the correlation between them, itfeature vector sequence y. To improve quality of synthetic

potentially produce unnatural articulatory movements.

speech, we also consider the global variance [15] in the production mapping.

3. 2 Manipulation

methodconsideringinter-

dimensional correlation of articulatory pa-

3. Articulatory Controllable Speech Modiﬁcation

rameters
In order to take into account the inter-dimensional correlation of articulatory parameters, we propose another method

Figure 1 depicts process ﬂow of the proposed articulatory controllable speech modiﬁcation system.

that performs two steps of the inversion mapping. In the ﬁrst

Firstly, an

step, the articulatory parameters are estimated from the in-

input speech signal is analyzed and parameterized into aversion mapping and then they are manipulated by using thetime sequence of the mel-cepstrum parameters c and thatﬁrst method as explained previously. In the second step, theof the source excitation parameters s, i.e. log-scaled wave-

modiﬁed parts of the articulatory parameters are attached toform power and log-scaled F0 . After extracting a time se-

the source features, then the inversion mapping is performed

quence of the mel-cepstral segment feature vectors O, thatto reﬁne unmodiﬁed parts of the articulatory parameters.

of the articulatory parameters x is estimated from it by us-

At frame t, the modiﬁed parts of the articulatory pa(m)ing the inversion mapping described in Section 2. 1. Then,

rameters are denoted as x̂t

the estimated articulatory parameters are manipulated as we

joint static and dynamic feature vectors is denoted as X̂

want. Subsequently, by using the production mapping de-

Whereas, the unmodiﬁed parts of the articulatory param-

scribed in Section 2. 2, a time sequence of the correspond-

eters at frame t are denoted as xt

ing mel-cepstrum parameters ĉ is estimated from that of theand a time sequence of their

(u)

(m)

.

and a time sequence—3—

of their joint static and dynamic feature vectors is denoted
as X̂

(u)

. It has to be noted that the sum of dimensions of(m)the unmodiﬁed parts x̂t(u)and the modiﬁed parts xt

We conducted both objective and subjective evaluations.
The objective evaluation was conducted to investigate theisoptimum number of mixture components in the inversion

equivalent to D. Considering the modiﬁed parts as the given

mapping and the production mapping and show accuracy

features like the source features, the unmodiﬁed parts areof individual mapping processes. We conducted two sub-determined as follows:

jective evaluations to evaluate the performance of the pro-x̂(u) = arg max P(u)x

X (u) |O, X̂(m)

, m̂(O) , λ(O,X) , (10)subject to X (u) = W (xwhere W (x

(u)

(u)

)

x(u) , (11)posed system.The ﬁrst subjective evaluation was conductedto compare two articulatory manipulation methods describedin Sections 3. 1 and 3. 2 in terms of speech quality. Second
subjective evaluation was conducted to evaluate capability)

is a linear transformation matrix for creatingof the proposed system to modify particular vowel sounds asa time sequence vector of the joint static and dynamic fea-

we want by manipulating articulatory movements based onture vector of the unmodiﬁed articulatory parameters X̂

(u)from that of their static feature vector x(u) .
The inter-dimensional correlation of the articulatory parameters is modeled by the mixture-dependent full covariance matrices in the inversion mapping. Consequently, inEq.

our linguistic knowledge. The number of listeners was ten ineach subjective evaluation.
4. 2 Objective evaluation of inversion and production mapping
In the inversion mapping, we calculated a correlation co-(10) the unmodiﬁed articulatory parameters are re-eﬃcient between the estimated articulatory parameters andﬁned according to the modiﬁed parts. Note that not only

the natural articulatory parameters. The number of mix-

inter-dimensional correlation but also inter-frame correla-ture components was varied between 32, 64, and 128. Astion is considered in this reﬁnement process thanks to thea result, 64 mixture components gave the highest correla-trajectory-based conversion framework [1] using an explicit

tion coeﬃcient of 0.79 in the inversion mapping, which was

relationship between the static and dynamic features. There-

comparable to the previous result shown in [1]fore, it is expected that this manipulation method produces

In the production mapping, mel-cepstral distortion be-

more natural articulatory parameter trajectories than thetween the estimated mel-cepstra and the natural mel-cepstra

simple manipulation method.

was calculated. The number of mixture components was also

4. Experimental Evaluation

varied between 32, 64, and 128. As a result, 64 mixture
components also gave the lowest mel-cepstral distortion of

4. 1 Experimental conditions

4.70 dB in the production mapping.

We used STRAIGHT analysis method to calculate spec-

We also evaluated the estimation accuracy of mel-cepstrum

tral envelope at each frame t. Then, it was converted toin the proposed sequential inversion and production mappinga 25-dimensional mel-cepstrum, where 1st through 24th co-processes without performing any modiﬁcations of the esti-

eﬃcients were used as the spectral envelope parameters.

mated articulatory parameters. The number of mixture com-

To extract mel-cepstral segments for the inversion map-ponents was set to 64 in both the inversion mapping and theping, current ± 10 frames were used. As the source exci-

production mapping. The resulting mel-cepstral distortion

tation parameters, we used log-scaled F0 values extracted

between the estimated and natural mel-cepstra was 4.45 dB.

by ﬁxed point analysis [19] in STRAIGHT, which also in-

4. 3 Evaluation of speech quality for comparison

clude unvoiced/voiced binary features, and log-scaled wave-

of articulatory manipulation methods

form power values extracted from STRAIGHT spectra. For

The ﬁrst subjective evaluation was conducted to evaluate

the articulatory parameters, we used 14-dimensional EMA

the modiﬁed synthetic speech quality from the proposed sys-data provided in MOCHA as described in Section 2., whichtem. The modiﬁcation was applied to scale the tongue tip’s

are normalized to Z-scores (zero means and unit variance).

movement in y-axis. The scale value ranged from 1× to 5×

A set of simultaneously recorded acoustic-articulatory datafrom the originally estimated articulatory parameters. Givenof a British male speaker data set provided in MOCHA [14]15 distinct sentences, each listeners then evaluated each sen-was used. We used 350 sentences for training and 110 sen-tence with an opinion score ranged from 1-to-5 point (bad-tences not included in the training for evaluation. Silence

to-excellent). The modiﬁcation was applied by using each offrames were removed using phonetic segmentation included

the two proposed articulatory manipulation method.

in the MOCHA. Two GMMs were trained separately for the

The result is shown in Fig. 2. As the scaling value gets

inversion mapping and the production mapping, as describedlarger, the speech quality also degrades. However, the ma-

in Sections 2. 1 and 2. 2.

nipulation method considering inter-dimensional correlation—4—

Figure 2 Mean Opinion Score (MOS) on the quality of synthetic

Figure 4 Perception percentage of vowel in modiﬁed words re-speech modiﬁed by two proposed manipulation meth-sulting from manipulation of tongue’s height position

ods described in Sections 3. 1 and 3. 2 (after [18])(after [18])Figure 3 Three various tongue tip trajectories during word
“stems”; non-modiﬁed, tongue shifted +1.5 cm up, andtongue shifted -1.5 cm down for the center frame ofvowel /E/

still retain the quality of speech when scaled to 2, as its quality is similar to that of the unmodiﬁed one. This method also
alleviates the degradation of speech quality compared to thesimple method. Overall, the manipulation method considering inter-dimensional correlation is more eﬀective than thesimple manipulation method.
4. 4 Evaluation of phoneme sound modiﬁcation
In the second evaluation, we examined the possibility ofmodifying particular vowel sounds by manipulating corresponding articulatory movements. We performed similar experiments as in [2], where three front vowels in English wereconsidered, /æ/, /E/ and /I/. Tongue tip’s height conﬁguration has the most inﬂuence in producing these vowels.
In /I/ vowel, the tongue tip is placed to the highest position. In /æ/ vowel, the tongue tip’s height is the lowest.
Whereas in /E/ vowel, tongue tip’s height is between previous two. We picked 10 words from test data, where each of

Figure 5 Plot of frequency as a function of amplitude of original
vowel /E/ in word “stems” and its modiﬁcation resultsby shifting the tongue tip 1.5 cm higher and lower

done by shifting the tongue tip’s height value at the center frame of /E/ vowel ranged from -1.5 cm to +1.5 cm in
0.5 cm intervals. In order to produce smoothly varying articulatory trajectories after the manipulation, a cubic splineinterpolation is performed [20] among a center frame of theleft phoneme of the modiﬁed vowel, the modiﬁed frame (i.e.
the center frame of the modiﬁed vowel), and a center frameof the right phoneme of the modiﬁed vowel. An example ofthe manipulated articulatory trajectories is shown in Fig.
3. The remaining unmodiﬁed articulatory trajectories werealso reﬁned by using the manipulation method consideringinter-dimensional correlation.
The result is shown in Fig. 4. We can observe a clear
transition between /E/ and /I/, as the tongue tip’s heightgets higher. On the other hand, as the tongue tip’s heightgets lower, the transition from /E/ to /æ/ is not as clear as
in that from /E/ and /I/. However, we can still observe a
reasonable tendency that the perception rate of /æ/ soundthem contained /E/ vowel. The phoneme modiﬁcation was—5—increases. Although further improvements will be necessary,
the proposed system has a great potential to achieve manual modiﬁcation of phoneme sounds of input speech by intuitively manipulating unobservable articulatory parameters.
Figure 5 shows an example of all pole spectral envelope
at the center frame of the modiﬁed vowel. Tongue tip height
manipulation was performed for the vowel /E/ of the sample
word “stems”. It is shown that a shift of the tongue tip position from the original position to 1.5 cm higher (+1.5 cm)
makes the diﬀerence between the ﬁrst formant frequency (F1 )and the second formant frequency (F2 ) larger. It is consistent
with the intention to modify vowel /E/ to /I/, as the tongue
goes higher, vowel openness goes lower (related to F1 ) andvowel frontness goes higher (related to F2 ). Furthermore, italso shows the reasonable tendency that F1 value goes higher
and F2 value goes lower as the tongue gets lower to modify
vowel /E/ to /æ/.

5. Conclusions
This report has presented a novel speech modiﬁcation system based on a sequential inversion and production mapping
process with Gaussian mixture models (GMMs). The modiﬁcation system enables us to modify speech signals by manipulating unobserved articulatory movements. We have proposed a manipulation method considering inter-dimensionalcorrelation of the articulatory movements to reﬁne unmodiﬁed parts according to the modiﬁed parts on the articulatory
parameter trajectories. Results of experimental evaluations
have demonstrated that 1) higher speech quality is producedby considering the inter-dimensional correlation when the articulatory manipulation is performed and 2) vowel sounds arewell modiﬁed by manipulating the corresponding articulatory parameters. We plan to improve quality of the synthetic
speech and controllability of the articulatory parameters.

6. Acknowledgements
This research was supported in part by JSPS KAKENHI
Grant Number 26280060.
References[1] Toda, T., Black, A. W., and Tokuda, K., “Statistical mapping between articulatory movements and acoustic spectrum using a gaussian mixture model,” Speech Communication, Vol. 50, No. 3, pp. 215–227, Mar. 2008.
[2] Ling, Z., Richmond, K., Yamagishi, J., and Wang, R., “Articulatory control of HMM-based parametric speech synthesis driven by phonetic knowledge,” IEEE Trans. Speech,
Audio, and Lang. Process., Vol. 7, No. 6, pp. 697–708, 2008.
[3] Parthasarathy, S., Schroeter, J., Coker, C. and Sondhi, M.
M., “Articulatory analysis and synthesis of speech,” FourthIEEE region 10 international conference, pp. 760–764, Nov.
1989.
[4] Wrench, A. A. and Richmond, K., “Continuous speech
recognition using articulatory data,” Proc. ICSLP, Beijing,

China, pp. 145–148, Oct. 2000.
[5] Bollepali, B., Black, A., and Prahallad, K., “Modeling anoisy-channel for voice conversion using articulatory features,” Proc. INTERSPEECH, Portland, USA, Sep. 2012.
[6] Schroeter, J. and Sondhi, M. M., “Speech coding basedon physiological models of speech production,” Advancesin Speech Signal Processing, S. Furui and M. M. Sondhi,
Marcel Dekker New York, pp. 231–267, 1992.
[7] Schroeter J. and Sondhi M. M., “Techniques for estimating
vocal-tract shapes from the speech signal,” IEEE Trans.
Speech and Audio Process., Vol. 2, pp. 133–150, 1994.
[8] Hogden, J., Lofqvist, A., Gracco, V., Zlokarnik, I., Rubin,P., and Saltzman, E., “Accurate recovery of articulator positions from acoustics: new conclusions based on humandata,” J. Acoust. Soc. Am., Vol. 100, pp. 1819–1834, 1996.
[9] Kaburagi, T. and Honda M., “Determination of the vocal
tract spectrum from the articulatory movements based on
the search of an articulatory-acoustic database,” Proc. ICSLP, pp. 433–436, Sydney, Australia, Dec. 1998.
[10] Hiroya, S. and Honda, M., “Estimation of articulatory
movements from speech acoustics using an HMM-based
speech production model,” IEEE Trans. Speech and Audio
Process., Vol. 12, No. 2, pp. 175–185, 2004.
[11] Hiroya, S. and Honda, M., “Speaker adaptation method
for acoustic-to-articulatory inversion using an HMM-based
speech production model,” IEICE Trans. Inf. and Syst., Vol.
E87-D, No. 5, pp. 1071–1078, 2004.
[12] Kello, C. T. and Plaut, D. C., “A neural network modelof the articulatory-acoustic forward mapping trained onrecordings of articulatory parameters,” J. Acoust. Soc. Am.,
Vol. 116, No. 4, pp. 2354–2364, 2004.
[13] Richmond, K., King, S., and Taylor, P., “Modelling the uncertainty in recovering articulation from acoustics,” Computer Speech and Language, Vol. 17, No. 2, pp. 153–172,
2003.
[14] Wrench, A., “The MOCHA-TIMIT articulatory database”,
http://www.cstr.ed.ac.uk/artic/mocha.html, Queen Margaret University College, 1999.
[15] Toda, T., Black, A. W., and Tokuda, K., “Voice conversion
based on maximum likelihood estimation of spectral parameter trajectory”, IEEE Trans. Audio, Speech and Lang. Process., Vol. 15, No. 8, pp. 2222–2235, 2007.
[16] Ben Youssef, A., Badin, P., and Bailly, G., “Can tongue berecovered from face? The answer of data-driven statisticalmodels”, Proc. INTERSPEECH, pp.2002-2005, Makuhari,
Japan, Sep. 2010.
[17] Kawahara, H., Masuda-Katsuse, I., and de Cheveigne, A.,“Restructuring speech representation using a pitch-adaptive
time-frequency smoothing and an instantaneous-frequencybased F0 extraction: possible role of a repetitive structure
in sounds”, Speech Communication, Vol. 27, No. 3-4, pp.
187-207, 1999.
[18] Tobing, P. L., Toda, T., Neubig, G., Sakti, S., Nakamura, S., and Purwarianti, A., “Articulatory Controllable
Speech Modiﬁcation Based on Statistical Feature Mappingwith Gaussian Mixture Models”, Proc. INTERSPEECH,
pp. 2298-2302, MAX Atria, Singapore, Sep. 2014.
[19] Kawahara, H., Katayose, H., de Cheveigne, A., and Patterson, R. D., “Fixed point analysis of frequency to instantaneous frequency mapping for accurate estimation of F0
and periodicity”, Proc. EUROSPEECH, pp. 2781-2784, Budapest, Hungary, Sep. 1999.
[20] Wolberg, G., “Cubic Spline Interpolation: A Review”, Department of Computer Science, Columbia University, New
York, NY, Technical Report CUCS-389-88, 1988.

—6—