2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)REGRESSION APPROACHES TO PERCEPTUAL AGE CONTROLIN SINGING VOICE CONVERSION
Kazuhiro Kobayashi1 , Tomoki Toda1 , Tomoyasu Nakano2 , Masataka Goto2 ,Graham Neubig1 , Sakriani Sakti1 , Satoshi Nakamura1
Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Japan1
National Institute of Advanced Industrial Science and Technology (AIST), Japan2
ABSTRACTThe perceptual age of a singing voice is the age of the singer asperceived by the listener, and is one of the notable characteristics
that determines perceptions of a song. In this paper, we describe
a novel voice timbre control technique based on the perceptual age
for singing voice conversion (SVC). Singers can sing expressively
by controlling prosody and voice timbre, but the varieties of voicesthat singers can produce are limited by physical constraints. Previous work has attempted to overcome the limitation through the useof statistical voice conversion. This technique makes it possible toconvert singing voice timbre of an arbitrary source singer into thatof an arbitrary target singer. However, it is still difficult to intuitively control singing voice characteristics by manipulating parameters corresponding to specific physical traits, such as gender andage. In this paper, we develop a technique for controlling the voicetimbre based on perceptual age that maintains the singer’s individuality. The experimental results show that the proposed voice timbre
control method makes it possible to change the singer’s perceptual
age while not having an adverse effect on the perceived individuality.
Index Terms— singing voice conversion, perceptual age, voicetimbre control, regression approaches, singer’s individuality.
1. INTRODUCTION
The singing voice is one of the most expressive components in music. In addition to pitch, dynamics, and rhythm, singers can express
more varieties of expression than other music instruments by using
the linguistic information of the lyrics. However, singers usually
have difficulty in changing their voice timbre widely, due to physical constraints in speech production. If it would be possible to freely
generate voice timbre beyond these physical constraints, it will open
up entirely new forms of expression in music.
Singing synthesis systems such as Vocaloid [1] and Sinsy [2],which produce a synthesized singing voice from note-level score information of the melody with its lyrics have been successfully applied as new music instruments. VocaListener [3] has been proposed
to synthesize more expressive singing voices, similar to those sungby actual singers. Towards manual control of expressive singing
voices, Nose et al. applied style control techniques [4, 5] in statistical parametric speech synthesis with hidden Markov models (HMM)[6] to singing synthesis [7]. This technique makes it possible to control singing styles based on a word pair of “child-like – adult-like”.
These systems are used in place of actually singing, therefore it isdifficult to directly control the singer’s voice timbre in real-time.
A number of techniques to control voice timbre have been proposed in singing voice conversion (SVC). One typical method is
SVC based on speech morphing [8] in the speech analysis/synthesis
framework [9]. This method makes it possible to independently

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morph several acoustic parameters between singing voices of different singers or different singing styles. One of the limitations of thismethod is that the morphing can only be applied to singing voicesamples of the same song.
To make more flexible SVC possible, statistical voice conversion(VC) techniques [10, 11] with Gaussian mixture model (GMM) havebeen successfully applied to convert the source singer’s singing voice
into another target singer’s singing voice [12, 13]. These techniques
make it possible to convert the spectral features of the source singer’s
singing voice into those of the target singer’s singing voice in any
song, keeping the linguistic information of the lyrics unchanged.
Furthermore, several attempts have been performed in order to implement more expressive voice timbre control techniques (describedin Section 2). However, it has still been difficult to control voicetimbre based on intuitive parameters in SVC.
In this paper, we focus on the perceptual age, or the age thata listener predicts the singer to be, and develop a method to control this intuitively understandable parameter in SVC. In previous
work, we have reported that both prosodic features (e.g., F0 pattern)
and spectral features have an effect on perceptual age, and prosodic
features more strongly affect the perceptual age than spectral features but they also cause an adverse effect on the perceived singer’s
individuality [14]. In traditional SVC [13, 15], only the spectralfeatures such as mel-cepstrum are converted. It is straightforward
to develop a real-time SVC system capable of controlling the perceptual age of singing voices, applying statistical VC with multipleregression GMM (MR-GMM) [16] and real-time statistical VC techniques [17, 18] to SVC. On the other hand, it is not straightforward totreat the prosodic features for controlling perceptual age in real-time
SVC. Furthermore, the adverse effect on the perceived singer’s individuality in the prosodic feature conversion needs to be alleviatedto intuitively control only the perceptual age. Based on these facts,
although controlling prosodic features makes it possible to change
singer’s perceptual age widely, we only treat spectral features as aacoustic cue in this paper.
In this paper, we apply statistical VC with MR-GMM to SVC toachieve the perceptual age control in SVC. The standard MR-GMM
has difficulty maintaining the individuality of the source singer, because the subspace of the MR-GMM only expresses the average
voice timbre of training singers. To solve this problem, we propose anovel voice timbre conversion method that modifies the MR-GMM
to convert the singer’s perceptual age while retaining singer’s individuality in SVC.
2. RELATED WORK
SVC with GMM is only capable of converting acoustic features between a pair of trained source and target singers. To develop a more
flexible SVC system, eigenvoice conversion (EVC) techniques [19]

have been applied to SVC [15]. In a SVC system based on many-tomany EVC [20], which is one particular variety of EVC, an initialconversion model called the canonical eigenvoice GMM (EV-GMM)
is trained in advance using multiple parallel data sets including songpairs of a single reference singer and many other singers. The EVGMM is adapted into arbitrary source and target singers by automatically estimating a few adaptive parameters from the given singing
voice samples of those singers. Although this system is also capableof flexibly changing singing voice timbre by manipulating the adaptive parameters even if no target singing voice sample is available, it
is difficult to achieve the desired singing voice timbre, because it ishard to predict the change of singing timbre caused by the manipulation of each adaptive parameter.
3. STATISTICAL SINGING VOICE CONVERSION (SVC)
3.1. SVC with GMM
SVC with GMM is a technique that converts the voice timbre of asource singer into that of a target singer. SVC with GMM consistsof a training process and a conversion process.
In the training process, a joint probability density function of
acoustic features of the source and target singers’ singing voices ismodeled with a GMM using a parallel data set in the same manner as
in statistical VC for normal voices [13]. As the acoustic features ofthe source and target singers, we employ 2D-dimensional joint static⊤ ⊤and dynamic feature vectors X t = [x⊤t , ∆xt ] of the source and⊤ ⊤Y t = [y ⊤
,
∆y
]
of
the
targetconsistingofD-dimensional staticttfeature vectors xt and y t and their dynamic feature vectors ∆xt and∆y t at frame t, respectively, where ⊤ denotes the transposition of
the vector. Their joint probability density modeled by the GMM isgiven byP (X t , Y t |λ)"# $#$% (XX) (XY ) &'M!
X t µ(X)
Σm Σm
m
=
αm N
;
,
) ,
(Y X)
(Y Y )
Y t µ(Y
Σm Σm
m
m=1

Many-to-many SVC is a technique to convert acoustic features of anarbitrary source singer into those of an arbitrary target singer [15].
Many-to-many SVC with EV-GMM also consists of a training process and a conversion process.
In the training process, the joint probability density of reference
and target features is modeled with the EV-GMM as follows:()(s)
P X t , Y t |λ(EV ) , e(s)"#$ # (X) $ % (XX) (XY ) &'
M
!
Xt
µm
Σ
Σm
=
αm N
, m
, (3)
(s) ;
(Y )
(Y X)
(Y Y )
Yt
µ
(s)
Σ
Σm
m
m
m=1
)
(s)
µ(Y
+ cm ,
m (s) = Am e
(s)

(1)

(2)

where W is a transformation matrix to expand the static feature
vector sequence into the joint static and dynamic feature vector sequence [21]. The conditional probability density function
P (Y |X, λ) is analytically derived from the GMM of the joint
probability density given by Eq. (1). To alleviate the oversmoothing
effects that usually make the converted speech sound muffled, global
variance (GV) [11] is also considered in conversion.

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(s)

(s)

(4)

⊤

where e = [e (1), · · · , e (J)] is the target singer-dependent
weight parameter for controlling target voice timbre. λ(EV ) is aparameter set of the canonical EV-GMM consisting of the bias vector
cm , and the basis vectors Am = [am (1), · · · , am (J)] for the mth mixture component, where the number of basis vectors is J in
addition to the parameter set of the GMM. Acoustic features of anarbitrary target singer are modeled by setting only e(s) to the singer’s
specific values.
In the conversion process, the joint probability density of the(i)acoustic features between the source singer’s voice Y t and the tar(o)get singer’s voice Y t is derived as
(
)
(i)
(o)
P Y t , Y t |λ(EV ) , e(i) , e(o)
=

M
!

m=1

where N (·; µ, Σ) denotes the normal distribution with a mean vector µ and a covariance matrix Σ. The mixture component index ism. The total number of mixture components is M . λ is a GMM
parameter set consisting of the mixture-component weight αm , themean vector µm , and the covariance matrix Σm of the m-th mixture
component. A GMM is trained using joint vectors of X t and Y t in
the parallel data set, which are automatically aligned to each otherby dynamic time warping.
In the conversion process, the source singer’s singing voice isconverted into the target singer’s singing voice with the GMM using
maximum likelihood estimation of speech parameter trajectory [11].
Time sequence vectors of the source features and the target features
⊤ ⊤
⊤
⊤ ⊤
are denoted as X = [X ⊤1 , · · · , X T ] and Y = [Y 1 , · · · , Y T ]where T is the number of frames included in the time sequence ofthe given source feature vectors. A time sequence vector of the con⊤ ⊤verted static features ŷ = [ŷ ⊤1 , · · · , ŷ T ] is determined as follows:ŷ = argmax P (Y |X, λ) subject to Y = W y,y3.2. Many-to-Many SVC with EV-GMM

(
)*
(
)
(i)
P m|λ(EV )P Y t |X t , m, λ(EV ) , e(i)

(
) (
)
(o)
P Y t |X t , m, λ(EV ) , e(o) P X t |m, λ(EV ) dX t"%&&'
$ % (Y Y )M(i) # (Y )(Y XY )!
Yt
µm (i)
Σm
Σm
=
αm N
;
,
, (5)
(o)
)
(Y XY )(Y Y )
µ(Y
Yt
Σm
Σm
m (o)
m=1
XY )
X) (XX)
Σ(Y
= Σ(Y
Σm
m
m
(i)

−1

)
Σ(XY
,
m

(6)

(o)

where e and e are adaptive parameters of the source and target
singer. Using this many-to-many EV-GMM, the converted singing
voice is generated in the same manner as described in Section 3.1.
4. SVC CONSIDERING PERCEPTUAL AGE
In this paper, we employ VC with MR-GMM [16] for developing
an SVC system that can control perceptual age. We first apply the
MR-GMM to SVC to make it possible to control voice timbre basedon perceptual age. Then, we apply the conversion process of manyto-many SVC to SVC with MR-GMM to more flexibly develop the
MR-GMM for an arbitrary source singer. Furthermore, we implement a perceptual age control technique to keep singer’s individuality by changing the representative form of the target mean vector.
4.1. SVC with Multiple Regression GMM (MR-GMM)
SVC with MR-GMM is a voice timbre control technique that manipulates the voice timbre of a target singer by intuitive parameters.
SVC with MR-GMM consists of a training process and a conversion
process.
In the training process, a joint probability density function of
acoustic features of the source and many pre-stored target singers’
singing voices are modeled with MR-GMM using parallel data sets

Table 1. Relationship of features and mean vectors of MR-GMM and Modified MR-GMM.
MethodSource features
Target feature
Target mean vector(Y ))MR-GMM
Singing voice, w(o)Average singing voice of the age of w(o)bm w(o) + µ(Y
m
(Y )
(Y )
(i)
(i)
Modified MR-GMM Singing voice of the age of w , ∆w Singing voice of the age of (w + ∆w)µ̂m + bm ∆w
in the same manner as in statistical VC with MR-GMM for normal
speech [16]. The joint probability density modeled by the MR-GMM
is given by()(s)
P X t , Y t |λ(M R) , w(s)"#$# (X) $% (XX) (XY ) &'
M
!
Xt
µm
Σ
Σm
=
αm N
, m
.
(s) ;
(Y )
(Y X)
(Y Y )
Yt
µ
(s)
Σ
Σm
m
m
m=1

)
(Y )
µ(Y
m (i) = µ̂m ,

(7)

(8)

(Y )

)
where bm and µ(Ym indicate the representative vector and bias vector respectively. w(s) indicates the s-th pre-stored target singer’s
perceptual age score, which is manually assigned for each pre-stored
target singer.
In the conversion process, the perceptual age score is manually
set to a desired value. Then, the source singer’s singing voice is converted into the target singer’s singing voice based on the perceptual
age score with the MR-GMM in the same manner as the conversion
process in Section 3.1.

4.2. MR-GMM implementation based on Many-to-Many SVC
To implement voice timbre control based on the perceptual age foran arbitrary source singer, we adapt many-to-many SVC [15] to SVC
based on MR-GMM. The joint probability density of the many-tomany MR-GMM is as follows:
(
)
(i)
(o)
P Y t , Y t |λ(M R) , w(i) , w(o)
=

M
!

m=1

(
)*
(
)
(i)
P m|λ(M R)
P Y t |X t , m, λ(M R) , w(i)

(
) (
)
(o)
P Y t |X t , m, λ(M R) , w(o) P X t |m, λ(M R) dX t"%&$% (Y Y ) (Y XY ) &'M(i) # (Y )!
Yt
µm (i)
ΣΣ
=αm N, (YmXY ) m(Y Y )
(9)
(o) ;
(Y )
µ
(o)
Y
Σ
Σm
m
m
t
m=1
XY )
X) (XX)
Σ(Y
= Σ(Y
Σm
m
m

−1

)
Σ(XY
,
m

(10)

where w and w indicate the perceptual age score of the source
singer and the target singers, respectively. Source and target mean
vectors are given by Eq. (8).
In SVC with many-to-many MR-GMM, it is possible to use Eq.
)(8) to describe the source mean vectors µ(Ym (i) based on the perceptual age score of the source singer. However, accuracy of acoustic
modeling by the MR-GMM tends to decrease because the acoustic
characteristics of the source singer are not always modeled well on
a subspace spanned by the basis vector. To develop a better MRGMM for the source singer who is not included among the training
singers of the MR-GMM, we assume an ideal condition that the parallel data of the source and reference singers, which are used in the
training of MR-GMM is available. This condition is still practical in
the development of an SVC system that can control perceptual ageof the source singer. Using parallel data between the source and the
(i)

(11)

)
where µ̂(Ym is its maximum likelihood estimate.

4.3. Modified MR-GMM to Retain Singer Individuality

The mean vector of the s-th prestored singer is given by)(Y ) (s)
)
µ(Y
+ µ(Y
m (s) = bm w
m ,reference singers, the source mean vector of the MR-GMM is updated according to the maximum likelihood criterion. Consequently,
the source mean vector is given by

(o)

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In SVC with many-to-many MR-GMM, it is possible to convert
voice timbre of the source singer into voice timbre corresponding
to the desired perceptual age score. However, the target mean vector
given by Eq. (8) only expresses average voice timbre of several prestored target singers. Therefore, the converted singing voice doesn’t
express voice timbre of the source singer.
For the purpose of developing SVC based on perceptual age
while retaining the source singer’s individuality, we change the representative form of the target mean vector as follows:
)
µ(Y
m (o)

=

) (o)
)
b(Y
+ µ(Y
m w
m

=

)
(i)
)
b(Y
+ ∆w) + µ(Y
m (w
m

=

) (i)
)
(Y )
b(Y
+ µ(Y
m w
m + bm ∆w

≃

)
(Y )
µ̂(Y
m + bm ∆w,

(12)
(o)

where the perceptual age score of the target singing voice w is represented by that of the source singing voice w(i) and the differencein perceptual age score ∆w between them. In the modified representative form, the target mean vector is represented by the source mean)
vector µ̂(Ym and the additional vector corresponding to a difference)
perceptual age score ∆w. As the source mean vector µ̂(Ym is directly(Y ) (i))
used instead of its projection on the subspace bm w + µ(Ym , it is
expected that acoustic characteristics of the source singer’s singing
voice are preserved in this modified representative form.
5. EXPERIMENTAL EVALUATIONS
We define the conversion method in Section 4.2 as MR-GMM andin Section 4.3 as Modified MR-GMM in this experiment. Table 1
shows the source and target features of MR-GMM and Modified
MR-GMM.
5.1. Experimental Conditions
We used the AIST humming database [22] consisting of sungs withJapanese lyrics sung by Japanese male and female amateur singers
in their 20s, 30s, 40s, and 50s. The sampling frequency was set to
16 kHz. The 1st through 24th mel-cepstral coefficients extracted by
STRAIGHT analysis were used as spectral features. As the source
excitation features, we used F0 and aperiodic components in fivefrequency bands, i.e., 0–1, 1–2, 2–4, 4–6, and 6–8 kHz, which werealso extracted by STRAIGHT analysis [23]. The frame shift was 5
ms.
In the training of the MR-GMM, we prepared parallel data setsof a single female reference singer in her 20s and 27 male and 27
female singers in their 20s, 30s, 40s and 50s as pre-stored target
singers not included in the 16 evaluation singers. The number oftraining sungs was 25 for each singer. The length of each song wasapproximately 20 seconds. The perceptual age score for each singer

5.2. Experimental Results
Figure 1 shows the relationship between the age change setting andactual perceived perceptual age change in the Modified MR-GMM.
For settings from -60 to 60, the perceptual age of the singer varied
almost linearly. Especially, we can see the same tendency as observed in the investigation of segmental features shown in Figure 3
of [14], where changing segmental features from those of a 20-yearold singer to those of a 60-year-old singer resulted in a linear changeof about 5 years. This indicates that the Modified MR-GMM is able
to achieve a change in perceptual age similar to that achieved using
natural spectral parameters of a singer in a different age group.
Figure 2 indicates the result of the XAB test for the singer individuality. We can see that as we make larger changes in perceptual
age, the preference score of the Modified MR-GMM tends to decrease. However Modified MR-GMM has a higher preference scorethan the MR-GMM for each setting.
Figure 3 indicates the results of MOS test for the naturalness.
This figure has the same tendency as displayed in Figure 2. The
Modified MR-GMM has a higher MOS than the MR-GMM for each)setting. The bias vectors of the Modified MR-GMM (µ̂(Ym in Eq.
(12)) model voice timbre of the source singer. On the other hand,)those of the MR-GMM (µ(Ym in Eq. (12)) model voice timbre ofmultiple pre-stored target singers. Therefore, over-smoothing effectsof the MR-GMM tend to be larger than those of the Modified MRGMM. Consequently, the naturalness of the sungs is also improvedby using the Modified MR-GMM.
These results suggest that 1) the Modified MR-GMM enables tocontrol the perceptual age of sungs relatively well, 2) the Modified
MR-GMM is able to retain the singer individuality better than the

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Difference in perceptual age
after conversion

15
1095% confidence interval
Regression line

5
0
-5
-10
-15
-60

-40

-20

0

20

40

60

Perceptual age conversion setting
Fig. 1. Setting and actual differential in perceptual age after conversion.

Preference score [%]

100
80
60
40
MR-GMM
Modified MR-GMM
95% confidence interval

20
0
-60

-30

0

30

60

Perceptual age conversion setting
Fig. 2. Comparing singer individuality of MR-GMM and Modified
MR-GMM converted singing voice.
5

Mean opinion score

was determined as an average score over 25 sungs of the singer rated
by one male subject in his 20s [14]. The number of mixture components of the MR-GMM was 128 for spectral envelope and 32 foraperiodic components.
First, we evaluated the extent to which MR-GMM was able toperceivably change perceptual age. Eight male subjects in their 20s
were divided into two groups, and the 16 evaluation singers weredivided into two groups so that one group always included one malesinger and one female singer in each age group. We changed thedifference perceptual age score in Eq. (12) into -60, -40, -20, 0, 20,
40 and 60. Subjects were asked to guess the age of each converted
singing voice by listening to it in random order.
In the second experiment, we conducted an XAB test on thesinger individuality of both MR-GMM and Modified MR-GMM.
Subjects and evaluation singers were separated into two groups inthe same manner as the first experiment. We changed perceptual age
score in Eq. (12) into -60, -30, 30 and 60 in the Modified MR-GMM.
In the MR-GMM, we varied the perceptual age score ±30, 60 in Eq.
(8) from the average perceptual age results in each evaluation singer
of intra-singer SVC (∆w = 0) in the prior experiment. A pair ofsongs generated by the MR-GMM and Modified MR-GMM of thesame singer and the specified variation of perceptual age scores waspresented to subjects after presenting the intra-singer SVC as a reference. Then, they were asked which voice sounded more similar to
the reference in terms of the singer individuality.
In the final experiment, we evaluated the naturalness of the converted singing voice using a mean opinion score (MOS). The numberof subjects and evaluation singers were the same as in the first experiment. The perceptual age score was the same as for the second
experiment. Subjects rated the naturalness of the converted sungs
using a 5–point scale: “5” for excellent, “4” for good, “3” for fair,“2” for poor, and “1” for bad.

4.5

Natural singing voice

MR-GMM
Modified MR-GMM
95% confidence interval

4
3.5
3
2.5
2
1.5
1
-60

-30

0

30

60

Perceptual age conversion setting
Fig. 3. Mean opinion score of MR-GMM and Modified MR-GMM.
MR-GMM during the perceptual age control, and 3) the Modified
MR-GMM also generates better quality converted sungs comparedwith the MR-GMM.
6. CONCLUSION
In order to develop voice timbre control based on perceptual age,
we have proposed a method for controlling perceptual age that retains the singer’s individuality. A conventional voice timbre control technique based on multiple-regression Gaussian mixture models (MR-GMM) was not able to control the singer’s perceptual age
while retaining the singer’s individuality. To solve this problem, we
proposed a modified version of the MR-GMM that overcomes thisproblem. The experimental results have demonstrated that the proposed method makes it possible to control the singer’s perceptual
age while preserving individuality.
Acknowledgments
This work was supported in part by JSPS KAKENHI Grant Number
22680016 and by the JST OngaCREST project.

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