AN ESTIMATION METHOD OF VOICE TIMBRE EVALUATION VALUES USING FEATURE
EXTRACTION WITH GAUSSIAN MIXTURE MODEL BASED ON REFERENCE SINGER
Soichi Yamane1 , Kazuhiro Kobayashi1 , Tomoki Toda2 ,Tomoyasu Nakano3 , Masataka Goto3 , Satoshi Nakamura1 ∗1

Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Japan
2
Information Technology Center, Nagoya University, Japan
3
National Institute of Advanced Industrial Science and Technology (AIST), Japan
ABSTRACTThis paper presents an estimation method of voice timbre evaluation
values for arbitrary singer’s singing voices generated with a singing
voice synthesis system towards the development of a singing voice
retrieval system. The voice timbre evaluation values are numerical values corresponding to voice timbre expression words, such as“Age” and “Gender”, and they usually need to be manually assigned
to individual singers’ singing voices through listening. To make itpossible to automatically estimate them from given singer’s singing
voices, an acoustic feature to well capture only each singer’s voicetimbre is extracted with a Gaussian mixture model trained using parallel data between singing voices sung by many pre-stored target
singers and same voices sung by a reference singer. Then, the voicetimbre evaluation values are estimated from the extracted feature using regression models. The experimental results showed that the
proposed method is capable of accurately estimating those values forsome expression words, such as “Age” and “Gender”, and nonlinear
regression is effective for the expression words, “Powerfulness” and“Uniqueness.”
Index Terms— singing voice synthesis, voice timbre, estimation of evaluation values, Gaussian mixture model, reference singer
1. INTRODUCTIONIn creating vocal music, a singing voice synthesis system, such as
VOCALOID [1], UTAU [2] or Sinsy [3], is often used by many end
users. The singing voice synthesis system allows the users to easily synthesize a singing voice as they want by manually inputtingscore information such as pitch, onset time, and duration to control melody and linguistic information to represent desired lyrics.
Moreover, it enables the users to easily change voice timbre of synthesized singing voices to not only manipulate control parametersof voice timbre but also select different singers’ singing voice data.
In particular, voice timbre of singing voices has a large impact on
the vocal music, and therefore, it is important for the users to carefully select it so that voice timbre of synthesized singing voices well
suits to the music created by the user. However, a large number ofavailable singing voice data exist currently and it tends to increase
more and more. For example, UTAU voice libraries [2] include over
5000 kinds of singing voice data [4]. Consequently, it is difficult tosearch for the most suitable singing voice data among them. It will
∗ We thank to Suzuki Serif who provided the voice timbre evaluation values intended for the UTAU voice libraries used in this experiments. This
work was supported in part by JSPS KAKENHI Grant Number 26280060
and by the JST OngaCREST project.

978-1-4799-9988-0/16/$31.00 ©2016 IEEE

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be helpful to develop a system to retrieve the suitable singing voicedata.
Music information retrieval has been widely studied and various methods focusing on singing voices have also been proposed.
For example, several music information retrieval systems based onvoice timbre similarity have been proposed to search for music data
including a singing voice of which voice timbre is similar to thatin input music data [5–10]. As another retrieval method, a singing
style retrieval method uses an input singer’s singing voice to searchfor singing voices sung in a similar singing style to that of the input
one based on a similarity measure calculated with a probability distribution on the phase plane (i.e., f0 −∆f0 plane) to express dynamic
variations of f0 patterns [11]. In these methods, the user basicallyneeds to input reference singing voice or music data as a query ofwhich a singing style or voice timbre is similar to the target one thatthe user wants to search for. Therefore, it is still difficult to search forthe desired singing voices used in the singing voice synthesis system
if the user cannot find such a suitable reference data.
To develop a singing voice retrieval method with no need of reference data, it is essential to define a measure to describe target characteristics of singing voices, such as a singing style or voice timbre.
In this paper, we focus on voice timbre because a singing style canbe well controlled by the users in the singing synthesis system. As
related work, there have been several attempts at describing voice
quality of speaking voices using an evaluation value on voice quality
expression words [12] and singing impression words [13]. According to the article [12], several word pairs expressing voice quality,
such as husky/clear (clearness) and elder/younger (age), have been
selected by applying factor analysis to a result of a large-scaled perceptual evaluation using many speakers’ natural voices. Each wordpair can be used to manually assign a 5-scaled evaluation value (-2:disagree, -1: disagree a little, 0: neither, 1: agree a little, 2: agree)
to describe voice quality of individual natural voices. It has been reported that this description method is helpful for developing a speechsynthesis system or a voice conversion system making it possible forthe users to intuitively control voice quality of synthesized/converted
speech as they want [14, 15].
Inspired by this conventional method, in this paper we also use
evaluation values for several word pairs to describe voice timbre ofindividual singers’ singing voices. These voice timbre evaluation
values will be effectively used to evaluate similarity of voice timbre
between different singing voice data, and also used to intuitively design input query for searching for singing voice data with the desired
voice timbre. On the other hand, in order to develop such a singing
voice data retrieval system, it is inevitable to assign the voice timbre
evaluation values to all existing singing voice data. However, these

ICASSP 2016

values basically need to be manually assigned to each singing voice
data through listening. In order to reduce a huge amount of effort
to do it, it is worthwhile to develop a technique for automaticallyassigning these values to the existing singing voice data.
In this paper, we propose an automatic estimation method of thevoice timbre evaluation values towards the development of a singing
voice data retrieval system helpful for the users to find their desired
singing voice data to be used in the singing voice synthesis system.
To extract an acoustic feature to well capture only voice timbre ofeach singing voice data, we use the joint probability density modeling method based on a reference singer [16], which was originally proposed as a voice conversion technique [17, 18] and thenwas successfully applied to singing voice conversion as well [19].
This method makes it possible to separately model voice timbre of
singing voices and acoustic variations caused by changes of phones
using a Gaussian mixture model (GMM) trained with parallel data
sets of synthesized singing voices between the reference singer andmany pre-stored target singers. After extracting the acoustic features of individual pre-stored target singers, regression analysis isperformed to develop an estimation model of the voice timbre evaluation values from the extracted acoustic feature. We conduct several
experimental evaluations to investigate 1) the effectiveness of the
proposed estimation method, 2) the effectiveness of using nonlinear
regression rather than linear regression, and 3) an effective way todevelop parallel data sets used for the GMM training.
2. VOICE TIMBRE FEATURE EXTRACTION BASED ON
JOINT PROBABILITY DENSITY MODELING WITH
REFERENCE SINGER
As acoustic features to capture voice timbre, we used segmental features, such as a spectral parameter and an aperiodic parameter. However, these features are also affected by phonemes and prosody (i.e.,
F0 and duration). Therefore, it is essential to remove their effects
on the acoustic features so as to represent only voice timbre. To doso, we apply the joint probability density modeling technique witha reference singer to the voice timbre feature extraction of singing
voice data used in a singing voice synthesis system. Figure 1 shows
an overview of the proposed feature extraction process.
First, parallel data sets of singing voices sharing the same score
information and lyrics between a single reference singer and many
pre-stored target singers are synthesized by using a singing voicesynthesis system. The segmental features are extracted frame byframe from each singing voice and they are time aligned between
each pre-stored target singer and the reference singer. Then, a joint
probability density function of these time-aligned segmental features
between the reference singer and the s-th pre-stored target singer ismodeled with a GMM as follows:()(s)
P Xt , Yt |µ(s) , λ([] [ (X) ] [ (XX) (XY ) ])
Xt
µm
Σ
Σm
=
αm N
, m
, (1)
(s) ;
(Y )
(Y X)
(Y Y )Ytµm (s) Σm Σm
m=1
[
]⊤
(Y )⊤
(Y )⊤
(Y )⊤
µ(s) = µ1(s), · · · , µm(s), · · · , µM (s) , (2)[]⊤ ⊤where Xt = x⊤
is a joint static and dynamic featuret , ∆xt
[
]⊤
(s)⊤
(s)⊤
(s)
vector of the reference singer, Yt = yt , ∆yt
is that of
the s-th pre-stored target singer, and ⊤ denotes transposition. The
normal distribution with a mean vector µ and a covariance matrix Σ
is denoted as N (·; µ, Σ). The total number of mixture components
M
∑

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is M and the mixture component index is m. The m-th mixture
component weight is αm . The mean vector of the m-th mixture(X)component consists of the reference singer’s mean vector µm and(Y )the s-th pre-stored target singer’s mean vector µm (s). The covariance matrix of the m-th mixture component consists of the reference(XX)singer’s covariance matrix Σm , the pre-stored target singer’s co(Y Y )(XY )variance matrix Σm , and their cross covariance matrices Σm(Y X)
and Σm . Note that these parameters except for the pre-stored
target singer’s mean vectors are shared among all pre-stored target
singers, and only the target mean vectors depend on individual prestored target singers. Therefore, they are concatenated to develop asuper vector µ(s) as the voice timbre feature vector of the s-th prestored target singer. The other parameters are included in a sharedparameter set λ.
To optimize these parameters, first a target-singer-independent
GMM is trained using all parallel data sets between the reference
singer and the individual pre-stored target singers as follows:
Ts
S ∏
{
}
(
)
∏
(s)
µ(0) , λ(0) = arg max
P Xt , Yt |µ, λ ,
{µ,λ}

(3)

s=1 t=1

where Ts is the number of time-aligned frames of the parallel data
set for the s-th pre-stored target singer and S is the total number ofthe pre-stored target singers. Then, a singer-dependent GMM for thes-th pre-stored target singer, which is given by Eqs. (1) and (2), istrained by updating only the super vector µ(0) using only the s-thparallel data set as follows:µ(s) = arg max
µ(0)

Ts
∏

(
)
(s)
P Xt , Yt |µ(0) , λ(0) .

(4)

t=1

It is noted that each mixture component consistently models thesame phone space over all singers, i.e., the reference singer and all
pre-stored target singers, thanks to the use of the parameters sharedover different singers and the use of parallel data in the parameter
optimization [16, 17]. In the joint density modeling, the reference
singer’s data plays a role as an anchor to align each mixture component to the same phone space over all pre-stored target singers. Consequently, acoustic variations caused by different phones are modeled with different mixture components and only singer-dependent
acoustic features are modeled with the super vector in Eq. (2). The
use of parallel data sets also sharing prosodic features as well as
linguistic information effectively makes differences of the resulting
super vector between different pre-stored target singers depend on
only differences of their voice timbre.
3. AUTOMATIC ESTIMATION OF VOICE TIMBREEVALUATION VALUE USING REGRESSION ANALYSIS
A regression analysis is applied to the estimation of voice timbre
evaluation values. First, we manually assign the voice timbre evaluation values for pre-determined word pairs to express voice timbre
into all of or a part of the pre-stored target singers. Then, we develop a regression model to estimate the voice timbre evaluation values from the voice timbre feature using the pre-stored target singers’
data.
3.1. Estimation Method based on Multiple Regression
The voice timbre evaluation values of the s-th pre-stored target
singer are stored as a voice timbre evaluation vector w(s) =
[
]⊤
(s)
(s)
(s)
w1 , · · · , w J, where wj is the voice timbre evaluation

Maximumlikelihood
estimation

Feature extraction

Mixture weight

Reference singer

CovariancematrixSD-GMMs

SI-GMM
Target singers

…

…

…

…
Joint features

Mean vector

Update mean
vectors oftarget singers

Mean vectors oftarget singers(Super vectors)

Fig. 1. Extraction of voice timbre features using parallel data
value for the j-th pre-determined word pairs, and the number ofvoice timbre evaluation values (i.e., the number of pre-determined
word pairs) is J. In multiple regression analysis, the voice timbre
evaluation vector w(s) is estimated from the corresponding super
vector µ(s) as follows:ŵ(s) = Aµ(s) + b,

(5)

where A and b are regression parameters, which are determinedwith minimum mean square error estimation between the given
voice timbre evaluation vectors (w1 , · · · , wS ) and the estimatedones (ŵ1 , · · · , ŵS ).
3.2. Estimation Method based on Kernel Regression
In kernel regression analysis, the voice timbre evaluation vector ofs-th target singer is estimated from the corresponding super vector
as follows:()ŵ(s) = V ⊤ ϕ µ(s) ,(6)where ϕ (·) is a function to map the super vector to a higher dimensional feature space, and V is a regression parameter in the higher
dimensional feature space, which is given by
V =

S
∑

(
)
ϕ µ(s) Zs⊤ ,

(7)

s=1

where Zs is a weighting parameter for the s-th mapped super vector.
From Eqs. (6) and (7), the estimate of w(s) is written as()ŵ(s) = Zk µ(s) ,
(8)
(
)
[ (
)
(
)]⊤
, (9)
k µ(s)=k µ(1) , µ(s) , · · · , k µ(S) , µ(s)where Z = [Z1 , · · · , ZS ], and k (·, ·) is a kernel function. In thispaper, we use Gaussian kernel as the kernel function, which is given
by
(
)
(
)
||x − x′ ||2,(10)
k x, x′ = exp −2
σ3.3. Estimation of Voice Timbre Evaluation Value for Arbitrary
Target Singer
In order to automatically estimate the voice timbre evaluation values
for a given target singer, first we generate a parallel data set between
the reference singer and the given target singer using a singing voicesynthesis system, and then, the target singer’s super vector is extracted in the same manner as shown in Eq. (4). Finally, the voicetimbre evaluation values are estimated from the extracted super vector using the trained regression model.
The proposed method can easily be applied to natural singing
voices as well. Thanks to the singing voice synthesis system, aparallel data set consisting of the reference singer’s singing voices
and the natural singing voices is easily developed by generating
the reference singer’s singing voices corresponding to the natural
ones. Therefore, the voice timbre evaluation values for the given
natural singing voice are estimated in the same manner as mentioned above. It is also possible to extract the super vector of thegiven natural singing voices without generating the parallel data
set. The probability
density) function of only the target acoustic((s)
features P Yt |µ(0) , λ(0) is easily derived from the target()(s)
singer-independent GMM P Xt , Yt |µ(0) , λ(0) in Eq. (4) bymarginalizing out the reference singer’s acoustic features Xt . Then,the super vector is optimized by using only acoustic features of
the given natural singing voices and the marginalized probability
density function. Well-known model adaptation techniques, such as
maximum a posteriori estimation [20], maximum likelihood linear
regression [21], and eigenvoice [22], are available in this optimization.
4. EXPERIMENTAL EVALUATION
4.1. Experimental Conditions

We used 40 kinds of UTAU voice libraries as the singing voice data.
Three different types of synthesized voices, “Monosyllabic voice”,“Talking voice”, and “Singing voice”, were generated by using
UTAU singing voice synthesis system and they were used for training and evaluation. “Monosyllabic voice” consisted of 100 kindsof Japanese syllables generated with 7 different F0 values, i.e., 700
where σ is a parameter of any positive value. The weighting paramsamples in total. “Talking voice” consisted of 50 synthesized voices
eter Z is determined with minimum mean square error estimation
generated by manually mimicking prosody of Japanese normal
also using regularization as follows:
speech in the ATR phoneme balanced sentence set [23]. “Singing−1
Z = W (K + rI) ,(11)voice” consisted of 60 phrases extracted from 10 songs using MIDI
[ (
)
(
)]⊤
[
] data of the RWC Japanese popular music database [24]. The length
(1)
(S)
(1)
(S)
where K = k µ,··· ,k µ,W = w ,··· ,w, of each sample of “Monosyllabic voice”, “Talking voice”, and“Singing voice” was about 2, 5, and 20 seconds, respectively.
and r is a regularization parameter.

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0.6
0.4
0.2
0

0.8
0.6
0.4
0.2
0

-0.2
AGE CLR GEN LSN POW UNQ
Voice timbre expression word

1
Correlation coefficient

0.8

Mel-cepstrumBand aperiodicity
Joint feature

1
Correlation coefficient

Correlation coefficient1

Mel-cepstrumBand aperiodicity
Joint feature

Monosyllabic voice
Talking voice
Singing voice

0.8
0.6
0.4
0.2
0

AGE CLR GEN LSN POW UNQ

AGE CLR GEN LSN POW UNQ

Voice timbre expression word

Voice timbre expression word

Fig. 2. Correlation coefficients between the estimated and target voice timbre evaluation values when using left) multiple regression, middle)
kernel regression, and right) different types of parallel data.
Table 1. Word pairs to express voice timbre
Specification
Word pairs
Adultness (AGE)
Young – Adult
Clearness (CLR)
Noisy – Clear
Gender (GEN)Feminine – Masculine
Listenability (LSN)
Lisping – LucidlyPowerfulness (POW)
Tender – Powerful
Uniqueness (UNQ)
Universality – Peculiarity

STRAIGHT analysis [25] was used to extract spectral envelope,which was further parameterized into the 1st through 24th melcepstral coefficients as a spectral feature. As an excitation feature,
STRAIGHT analysis [26] was also used to extract aperiodic components, which were averaged in five frequency bands, i.e., 0-1, 1-2,
2-4, 4-6 and 6-8 kHz. The frame shift was set to 5 ms. The sampling
frequency was set to 16 kHz. The number of mixture components
for the spectral feature was 128, and that for the aperiodic feature
was 16.
Table 1 shows 6 kinds of word pairs to express voice timbre
used in this evaluation. A 7-scaled evaluation value was used to describe voice timbre corresponding to each word pair (e.g. “Adultness(AGE)” is annotated from 1-Young to 7-Adult). 19 annotators manually assigned these values to individual 40 singers’ singing voice
data, and then values averaged over all annotators were used as thevoice timbre evaluation values.
For each acoustic feature (i.e., the spectral feature or the aperiodic feature), the target-singer-independent GMM was trained using
all singers’ singing voice data. Then, the super vector for each singer
was extracted. In regression analysis, leave-one-out cross-validation
was performed to train the regression model and evaluate its estimation accuracy of the voice timbre evaluation values. The parametersof the kernel regression were optimized manually.
4.2. Experimental Results
Figure 2 shows the results of estimation of voice timbre evaluation
values by the multiple regression (left graph) and the kernel regression (middle graph) when using “Monosyllabic voice” as the parallel data. Each figure shows results using the spectral feature (Melcepstrum), the aperiodic feature (Band aperiodicity), and their joint
feature as the acoustic feature modeled by the GMM. We can seethat the spectral feature yields higher estimation accuracy than theaperiodic feature. Even if using the joint feature, its estimation accuracy is almost the same as that of the spectral feature. Therefore,

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the aperiodic feature is not effective for the estimation of the voicetimbre evaluation values.
We can see that the correlation coefficients for “Adultness(AGE)” and “Gender (GEN)” are over 0.8 and 0.9, respectively. On
the other hand, very weak correlation is observed for “Clearness(CLR).” These results are consistent in both the multiple regression
and the kernel regression. On the other hand, the correlation coefficients for “Powerfulness (POW)” and “Uniqueness (UNQ)” tend
to be increased from 0.4 to 0.6 using the kernel regression rather
than the multiple regression. These results imply that there exists
nonlinearity in the mapping from the super vector into these voicetimbre evaluation values although the number of dimensions of thesuper vector is already quite high.
The result when using different types of the parallel data is also
shown in Fig. 2 (right graph). In this experiment, we used the joint
feature and the kernel regression. We can see that the difference of
estimation accuracy caused by using different types of the parallel
data is small except for estimation of the voice timbre evaluation
score on “Uniqueness (UNQ).” Because of these small differences,
we may flexibly select a type of parallel data according to that ofavailable singing voice data.
5. CONCLUSION
This paper has presented an estimation method of voice timbre evaluation values from given singing voice data used in a singing voicesynthesis system. To extract an acoustic feature to well represent
only voice timbre while minimizing the effects of acoustic variations
caused by different phones or prosody, we have successfully applied the joint probability density modeling using reference singer’s
singing voices to the proposed estimation process. The regression
analysis have also been used for estimating the voice timbre evaluation values from the extracted voice timbre feature. Experimental
evaluations for the estimation of 6 voice timbre evaluation values
for “Adultness”, “Clearness”, “Gender”, “Listenability”, “Powerfulness” and “Uniqueness” have been conducted, demonstrating that 1)very high estimation accuracy is achieved for “Adultness” (r > 0.8)
and “Gender” (r > 0.9) using the mel-cepstral coefficients as theacoustic feature in the proposed method; 2) improvements in estimation accuracy (from r = 0.4 to r = 0.6) is observed for “Powerfulness” and “Uniqueness”; and 3) various types of parallel data, suchas “Monosyllabic voice”, “Talking voice”, and “Singing voice”, canalso be used in the proposed method. We plan to develop a singing
voice retrieval system for a singing voice synthesis system using the
proposed method.

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