THE INSTITUTE OF ELECTRONICS,
INFORMATION AND COMMUNICATION ENGINEERSTECHNICAL REPORT OF IEICE.

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 sys-

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-

tems that cover this relationship [1].

—1—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,[]⊤ ⊤which is given by X t = x⊤
, where ∆xt is the dyt , ∆xt

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 unmodified

t, which is calculated as ∆xt = 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[]⊤ ⊤vector O ⊤constructed at each frame is used as thet , 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)
N
m

([
)
]⊤
⊤
(O,X)
(O,X)
O⊤
,
X
;
µ
,
Σ
,
t
t
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

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(O,X)
vectors µ(O,X)
and covariance matrices Σmfor individualmmapping models. The data are provided in MOCHA [14],(O,X), meanmixture components.

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.

ponent sequence m̂ is determined first by:()m̂(O) = arg max P m|O, λ(O,X) .

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,
where T is the number of frames and ⊤ denotes the transposition of the vector.

m(3)

After that, the estimated articulatory parameter sequence isdetermined by:

2. 1 Inversion mapping
In the inversion mapping, the spectral envelope parameters extracted from an input speech signal are converted into()
x̂ = arg max P X|O, m(O) , λ(O,X) ,(4)

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

the corresponding articulatory parameters.
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
at multiple frames around the current frame. At frame t, themel-cepstral segment feature vector is denoted by O t , which
is defined as[]⊤⊤⊤
O t = A c⊤
+ b,t−L , · · · , ct · · · , ct+Lfeature sequence vector x.
2. 2 Production mapping
In the production mapping, both the articulatory parameters 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 , ∆xt , st , ∆st ] at framet. Whereas, the target feature consists of a joint static anddynamic feature vector of mel-cepstrum C t = [ct , ∆ct ] 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

excitation parameters s. In the end, a modified speech sig-

by using vocoder.

([
)
]⊤
⊤
(Y,C)
(Y,C)
Y⊤
,
C
;
µ
,
Σ
,
t
t
m
m

In this framework, we often want to manipulate only the(6)

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

m=1

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) .
mulatory parameters are denoted as x̂′t . In order to mod(7)Then, the mel-cepstrum sequence vector ĉ is determined by:()
ĉ = arg max P C|Y , m(Y ) , λ(Y,C) ,(8)csubject to Y = W (y) y,

ify particular articulatory movements, only corresponding
dimensions of articulatory parameters are modified.

For

example, 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 firstFirstly, 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

and a time sequence of their

(u)

(m)

.

and a time sequence

ing mel-cepstrum parameters ĉ is estimated from that of the—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)
x̂t

(u)
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:()(m)′x̂(u) = arg max P X (u) |O, X̂
, m̂(O) , λ(O,X) , (10)jective evaluations to evaluate the performance of the pro-the unmodified parts

and the modified parts(u)xsubject to X
(u)where W (x

(u)

=W

(x(u) )(u)x

, (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 +1,5cm up, andtongue shifted -1,5cm 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 1,5cm 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 22680016.
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