8th ISCA Speech Synthesis Workshop • August 31 – September 2, 2013 • Barcelona, Spain

Investigation of intra-speaker spectral parameter variation and its prediction
towards improvement of spectral conversion metricTatsuo Inukai, Tomoki Toda, Graham Neubig, Sakriani Sakti, Satoshi Nakamura
Graduate School of Information Science, Nara Institute of Science and Technology,
tatsuo-i@is.naist.jp, tomoki@is.naist.jp,
neubig@is.naist.jp, ssakti@is.naist.jp, s-nakamura@is.naist.jp

Abstract
In spectral conversion of statistical voice conversion (VC),
distance-based measures between the converted and target spectral parameters are often used as evaluation or training criteria.
However, even if the same speaker utters the same sentence,the spectral parameters vary utterance by utterance, and thus,
spectral distance between utterances still exists. Moreover, theoriginal prosodic features of input speech are often kept unchanged in some VC systems, such those that function in realtime. In such cases, prosody of converted and target speechsamples are different, and these differences increases spectral
distance. These potential spectral variations are not consideredin the conventional evaluation/training criterion. Thus, by constructing criteria that consider this spectral difference improvements in sound quality can be expected. In this paper, we investigate intra-speaker spectral variation between utterances of thesame sentence. We also propose a method for predicting thisvariation from prosodic parameter differences between the corresponding utterances. We conduct experimental evaluations
using many speech samples of the same sentence uttered bya single speaker, with results demonstrating that the proposed
method effectively predicts the intra-speaker spectral variationfrom the observed prosodic changes.
Index Terms: voice conversion, training/evaluation criterion,
intra-speaker spectral variation, prosodic differences, prediction

1. Introduction
Statistical voice conversion (VC) is an effective techniquefor modifying acoustic parameters to convert non-linguistic orpara-linguistic information while keeping linguistic information
unchanged [1]. It was originally proposed for speaker conversion to change the voice uttered by a source speaker as if it is uttered by a specific target speaker [2]. Recent progress in VC has
achieved high-quality and real-time conversion [3]. These technologies can be used in various VC applications for augmenting human-to-human speech communication, such as speakingaid for vocally handicapped people [4, 5], silent speech interfaces [6], bandwidth extension [7], and singing voice effectors[8]. Improving VC performance has the potential to contribute
greatly to practical use of these applications.
In real-time VC systems for speaker conversion, short-term
speech features, such as spectral parameters, are mainly converted with little delay using complex conversion functions. On
the other hand, long-term speech features, such as F0 patterns,
are fundamentally difficult to convert in real-time. Therefore,
simple conversion functions, such as a global linear transform,
are often used to convert F0 values frame by frame. Consequently, performance of the real-time VC system strongly de-pends on spectral conversion.
As for spectral conversion, various conversion or evaluation
criteria have been proposed. One of the most standard criteria
is a (weighted) distance measure between converted and target
spectral parameters. It is used in the most widely used VC methods, with a Gaussian mixture models (GMM) based on minimum mean square error estimation [9] or maximum-likelihood
estimation [10]. Some sophisticated model training methods
using it as an optimization criterion have also been proposed[11, 12]. Recently the use of not only the distance-based criterion but also other criteria have been proposed. One of themis global variance (GV), which is the second order moment ofa spectral parameter trajectory [10]. It has been reported thatspeech quality and conversion accuracy for speaker individuality in converted speech are significantly improved by considering both the distance-based criterion and the GV. It has also
been reported that mutual information is also useful [13]. The
effectiveness of these criteria has also been confirmed in modeltraining [14, 15, 16]. These results suggest that it is useful touse additional criteria rather than only the distance-based criteria, although it causes a larger distance between converted andtarget spectral parameters (i.e., a larger conversion error).
Previous research has not carefully investigated how much
spectral distance is acceptable in VC. By considering theamount of acceptable spectral distance, it may be possible to automatically determine weight parameter controlling the balance
between the distance-based criterion and additional criteria. To
clarify the acceptable distance, we focus on intra-speaker spectral variation, which is the spectral distance observed when thesame speaker utters the same sentence many times. It is empirically known that intra-speaker spectral variation will not go
to zero between utterances. Moreover, it has been reported thatlarger prosodic changes cause larger spectral differences [17].
Therefore, the acceptable spectral distance possibly changes according to prosodic differences between the converted and target voices.
In this paper, we investigate intra-speaker spectral variation
using many speech samples of the same sentences uttered by asingle speaker. Mel-cepstral distortion [18] is used as a metric to capture the intra-speaker spectral variation. Moreover,
we propose a method to predict the intra-speaker spectral variation between two utterances from their differences of various
prosodic parameters. This prediction is useful to determine theacceptable spectral distortion in each utterance-pair and it has apotential to develop better training, conversion, and evaluation
metrics for spectral conversion in VC.

89

T. Inukai, T. Toda, G. Neubig, S. Sakti, S. Nakamura

2. Basic Procedure of VC

where F0 is the source speaker’s F0 value and F̂0 is the converted F0 value. The conversion model parameters are µ(s)and σ (s) , which are mean and standard deviation values of logscaled F0 values of the source speaker, and µ(t) and σ (t) , which
are those of the target speaker. Prosodic parameters, such as
shape of F0 pattern, phoneme duration, and power patterns, arekept unchanged in conversion. Note that it is also possible toconvert them if real-time conversion processing is not necessary
and linguistic contents are available. However, such a conversion is essentially difficult in real-time conversion processing
without any linguistic contents.
In the conversion processing as mentioned above, the converted speech is generated by the converted spectral parameters and globally transformed F0 values without any prosodicchanges. Therefore, ideal converted spectral parameters willbe spectral parameters of a speech sample uttered by the target
speaker so that its prosody is the same as that of the source
speaker. However, it is not straightforward to record suchspeech samples as in each utterance pair of the available parallel data the target speaker’s prosody is usually different fromthe source speaker’s prosody. Therefore, the target spectral parameters are not ideal ones. Nevertheless, in the traditional approach the spectral conversion model is basically trained so thatthe conversion error (i.e., the distance between the converted
spectral parameters and the target spectral parameters) in theparallel data set is minimized.

3. Investigation of Intra-Speaker Spectral
Parameter Variation
We investigate how much spectral parameters vary when a single speaker utters the same sentence and how much spectral parameters differ additionally by imitating prosody of other speakers.
3.1. Recording of speech samples
We recorded speech samples of the same sentence uttered bya single speaker. One Japanese male speaker uttered one sentence 200 times with his own prosody. He also uttered thesame sentence while imitating prosody of other reference speakers. The number of reference speakers was 24 (12 male and
12 female). To make it easy to imitate the utterances, 1)
analysis-synthesized speech samples were generated by converting F0 values of speech samples of the reference speakers using Eq. (1) to make their F0 ranges equivalent tothat of the male speaker and 2) they were presented to themale speaker as reference speech samples during the recording.
The male speaker recorded 8 utterances imitating each reference speaker’s prosody. A total of 192 speech samples wererecorded. The sampling frequency was 16 kHz.

90

Uttered imitating others’ prosody
Uttered by own prosody

2000
Frequency

In the statistical VC for speaker conversion, a parallel data set
consisting of utterance pairs of the source and target speakers
is used to train the conversion models for individual speech parameters. As the conversion model for spectral parameters, aconditional probability density function of the target speaker’s
spectral parameters given the source speaker’s spectral parameters is often modeled by a GMM. On the other hand, as theconversion model for F0 parameters, the following global linear transformation is often used:)
σ (t) (
log Fˆ0 = (s) log F0 − µ(s) + µ(t) ,
(1)
σ

2500

1500
1000
500
0
2.5

3

3.5
4
4.5
5
5.5
Mel-cepstral distortion [dB]6

6.5

Figure 1: Frequency distribution of mel-cepstral distortion between utterances of the same sentence uttered by the same
speaker.

3.2. Intra-speaker spectral parameter variation
The 1st through 24th mel-cepstral coefficients extracted by
STRAIGHT analysis [19] were used as spectral parameters.
Frame shift was 5 ms. Mel-cepstral distortion was calculated
by performing dynamic time warping (DTW) in each utterance
pair.
Figure 1 shows the frequency distribution of the melcepstral distortion for all utterance-pairs, one is a frequency
distribution for speech samples with the male speaker’s own
prosody and the other is that for speech samples with the different speakers’ prosody. We can see that even in the same sentence with the same speaker, the mel-cepstral distortion is not
0. For the speech samples with the speaker’s own prosody, themean value is 3.9 dB and the standard deviation value is 0.35
dB. On the other hand, for the speech samples with the different
speakers’ prosody, the spectral variation tends to be larger; itsmean value is 4.4 dB and its standard deviation value is 0.38
dB.
These results suggest that 1) it is not necessary to decrease
mel-cepstral distortion to 0 in VC and 2) as prosodic differences
between the source and target speakers are larger, a larger melcepstral distortion will be acceptable.

4. Prediction of Spectral Parameter
Variation
While the source and target speakers’ prosody is usually different from each other in available parallel data sets, it is ideal topredict target spectral parameters in each utterance-pair when
the target speaker imitates prosody of the source speaker. However, this is not straightforward to do. Although a method forpredicting spectral parameter changes according to F0 changes
has been proposed [17], it still needs training data consistingof many speech samples of the same linguistic contents utteredby the target speaker with different F0 values. It is laborious
work to additionally record such a data set. Therefore, we simplify the problem to be solved. We predict spectral distortion
between the original speech sample of the target speaker in theparallel data and a practically unavailable speech sample utteredby the target speaker while imitating prosody of the corresponding utterance of the source speaker. Namely, we predict not anunobserved spectral feature vector itself but its distance froman observed spectral feature vector. The predicted spectral dis-

8th ISCA Speech Synthesis Workshop • August 31 – September 2, 2013 • Barcelona, Spain

Figure 2: Prediction procedure of mel-cepstral distortion fromprosodic variation parameters.

(a) Time warping function

tortion is still useful as it shows acceptable spectral distortion
depending on prosodic differences in each utterance-pair.
As a first step to achieve such a prediction, in this paper
we propose a method for predicting the mel-cepstral distortion
from prosodic variation parameters capturing prosodic differences using many speech samples of the same sentence utteredby a single speaker. Figure 2 shows the prediction procedure.
Prosodic parameters extracted from an utterance pair and theprosodic variation parameters are calculated as an explanation
variable. The mel-cepstral distortion is also calculated in thisutterance-pair as a target variable. Then, the mel-cepstral distortion is predicted from the prosodic variation parameters. In
a practical application, the prosodic variation parameters arecalculated between the source and target speech samples ineach utterance pair for training or evaluation. Finally, the melcepstral distortion is predicted from these samples. The predicted mel-cepstral distortion is regarded as an acceptable distortion between the converted and target spectral parameters.
Note that this distortion varies utterance by utterance. It is inevitable to develop a sentence/speaker-independent prediction
model to make it possible to apply this prediction model in practical VC conditions.

difference in power patterns. These parameters take positive
values and only take zeros when prosody of two speech samples is completely the same as each other.

4.1. Prediction Model

4.2.1. Duration Distortion

A multiple linear regression model is used to predict the melcepstral distortion from the prosodic variation parameters as follows:

To capture a difference of total duration over an utterance, duration distortion is calculated as follows:m̂i,j = a⊤ pi,j + c,(2)

where m̂i,j is the predicted mel-cepstral distortion between the
ith utterance and the j th utterance, pi,j is a prosodic variation
parameter vector between these utterances, a is a regression coefficient vector, and c is a bias value. The regression coefficient
vector and the bias value are determined by the least square error estimation. In this paper, the mel-cepstral distortion and theprosodic variation parameters are calculated using only voiceactive frames, which are automatically extracted with normalized waveform power.
4.2. Prosodic Variation Parameters
Several prosodic variation parameters are used in the prediction.
Duration distortion and DTW distortion capture the differencein duration. Voiced/unvoiced error rate and F0 distortion capture the difference in F0 patterns. Power distortion captures the6

Reference (N2/N1)

Dynamic features

5
4
3
2
1
0

0

50

100

150 200 250 300 350 400 450 500
Frame index of utterance 1(b) Dynamic feature sequence of time warping function

Figure 3: Calculation of DTW distortion.

Ddur = log Nl − log Ns ,(3)

where Nl is the number of frames extracted from a longer utterance, and Ns is the number of frames extracted from a shorterutterance.
4.2.2. DTW Distortion
To capture the difference in local duration, DTW distortion is
calculated as shown in Figure 3. First, temporally dynamic
features of the time warping function are determined by DTW,
which is given by the slope of a regression line as shown in
a1 (t) in Figure 3(a), and calculated at each frame of each utterance. One example of the dynamic feature sequence over anutterance is shown in Figure 3(b). If there is no difference inlocal duration, the time warping function is represented as a line
and the slope at every frame is equivalent to its constant slope
N2 /N1 as shown in Figure 3(a). The DTW distortion is calculated as a difference between the dynamic features and the91

T. Inukai, T. Toda, G. Neubig, S. Sakti, S. Nakamura
constant slope as follows:

DDTW

=

v
u N1 (
)2
∑
1 u
N2
t
a1(t) −
2N1 t=1
N1v
u N2 (
)2
∑
1 u
N1
t
+
a2(t) −,
2N2 t=1N2(4)where N1 and N2 are the number of frames of utterance 1 andutterance 2, and a1 (t) and a2 (t) are the dynamic feature atframe t over the utterance 1 and utterance 2. All frame pairs
from frame t − 1 to t + 1 over the utterance 1 are used to fit a
regression line to calculate a1 (t). In a similar way, a2 (t) is also
calculated.
4.2.3. Voiced/Unvoiced Error Rate
To capture the difference of voiced/unvoiced frames, avoiced/unvoiced error rate between frames time-aligned by
DTW is calculated as follows:N

DU/V =

1∑
e(t),
N t=1

(5)

where N is the number of time-aligned frame pairs, e(t) is afunction that returns 0 when voice/unvoiced information is thesame at frame-pair t and returns 1 when they are different.
4.2.4. F0 Distortion
To capture the difference of F0 patterns, F0 distortion is calculated between time-aligned frames by DTW as follows:v
u Nv (
(
))2
∑
1 u
(1)
(2)
t
log(F0 (t)) − log F0 (t)DF 0 =,(6)
Nv t=1(1)where Nv is the number of voiced frame pairs, and F0 (t) and(2)
F0 (t) are F0 values of individual utterances at frame pair t.
(max)
We also calculate a maximum value (DF0 ) and a mini(min)mum value (DF0 ) of the absolute difference of log-scaled F0
in each utterance pair.
4.2.5. Power Distortion
To capture the difference of power patterns, power distortion is
calculated between time-aligned frames by DTW as follows:
v
uN
∑
1u
2
(p(1) (t) − p(2) (t)) ,(7)
Dpow = t
N t=1

where N is number of frame pairs, p(1) (t) and p(2) (t) are normalized power values of individual utterances at frame pair t.
(max)
We also calculate a maximum value (Dpow ) and a mini(min)mum value (Dpow ) of the absolute difference of the normalized power in each utterance pair.
4.3. Normalization of Speaker-Dependency
The prosodic variation parameters and the mel-cepstral distortion are affected by speaker individuality. To reduce the impactof speaker dependence on these parameters, all parameters are

92

Table 1: Prediction results of mel-cepstral distortions.
Regression
Correlationmodel
coefficient
Speaker-dependent
Sentence-dependent
0.76
Speaker-dependent
Sentence-independent
0.75
Sentence-independent
Speaker-independent without normalization
0.64
Sentence-independent
Speaker-independent
with normalization
0.72
normalized so that their mean and standard deviation values areequal to 0 and 1 in each speaker.
This normalization can be straightforwardly applied to theprosodic variation parameters (i.e., variable calculable from the
input) using the parallel data in practical VC conditions. On
the other hand, it is not straightforward to apply it to the melcepstral distortion (i.e., a target variable). Namely, the normalized mel-cepstral distortion is predicted but the unnormalized
mel-cepstral distortion is hard to predict. Nevertheless, the normalized mel-cepstral distortion is still effective to improve theconventional training, conversion, and evaluation criteria because it captures additional information about the acceptable
spectral distortion varying utterance by utterance.

5. Experiments
5.1. Experimental Conditions
We recorded speech data of 5 speakers (4 males, 1 female) in
the same way as described in Section 3.1. Male 1 uttered 6 sentences 200 times, and the other speakers uttered 4 sentences 50
times. These sentences were extracted from the ATR Japanese
speech database [20]. The 1st through 24th mel-cepstral coefficients extracted by STRAIGHT analysis [19] were used as
spectral parameters. F0 values were extracted by the F0 estimation method of STRAIGHT analysis [21]. The sampling
frequency was 16 kHz. Frame shift was 5 ms.
To evaluate prediction accuracy, we calculated a correlation
coefficient between the predicted mel-cepstral distortion and theobserved mel-cepstral distortion. We evaluated the following
four models:
1) speaker- and sentence-dependent models: a single prediction model was trained and evaluated for each speakerand each sentence,2) speaker-dependent and sentence-independent models: a single prediction model was trained and evaluated for eachspeaker using all of his/her sentences,
3) speaker- and sentence-independent models without normalization: a global prediction model was trained for all
speakers using their all sentences without normalization
described in Section 4.3,
4) speaker- and sentence-independent models with normalization: a global prediction model was trained for all speakers using their all sentences with the normalization.
In each case, five-fold cross validation was employed. All combinations of utterance-pairs of the same speaker and the samesentence were considered. In the speaker-independent model,
the number of utterances of Male 1 was reduced to the same
number of utterances of the other speakers. We also evaluatedthe effect of individual prosodic variation parameters on prediction accuracy by adding them as explanatory features one by
one in the speaker- and sentence-dependent model.

8th ISCA Speech Synthesis Workshop • August 31 – September 2, 2013 • Barcelona, Spain
Table 2: Correlation coefficients between individual prosodic difference parameters.
max
min
–
DDT W DU/V
DF0 DFmax
DFmin
Dpow Dpow
Dpow
0
0
Ddur
0.22
0.05
0.10
0.02
0.03
0.11
0.04
0.03
DDT W
–
0.25
0.29
0.23
0.05
0.27
0.16
0.04
DU/V
–
–
0.40
0.24
0.20
0.58
0.32
0.12
DF0
–
–
–
0.65
0.28
0.37
0.19
0.12
DFmax
–
–
–
–
0.09
0.26
0.17
0.07
0
DFmin
–
–
–
–
–
0.14
0.06
0.04
0
Dpow
–
–
–
–
–
–
0.63
0.20
max
Dpow
–
–
–
–
–
–
–
0.10
5.5

5.2
Target distortion[dB]

Target distortion [dB]

5
4.8
4.6
4.4
4.2
4

5
4.5
4
3.5

3.8
3.6

3
3.6

3.8

4

4.2 4.4 4.6 4.8
Predicted distortion [dB]5

5.2

Figure 4: Scatter diagram of target mel-cepstral distortion andthat predicted by sentence-dependent model (for Male 1).

5.2. Experimental results
Table 1 shows results of individual prediction models using
all prosodic variation parameters. The speaker- and sentencedependent model is capable of predicting the mel-cepstral distortion with accuracy of 0.76 in correlation coefficient. A scatterplot between the predicted mel-cepstral distortion and the observed mel-cepstral distortion is shown in Figure 4. We can see
a tendency that the prediction error is smaller as the observed
mel-cepstral distortion is also smaller. The speaker-dependentand sentence-independent model does not cause any adverse effects and its correlation coefficient is 0.75 as shown in Table 1.
Its scatter plot is shown in Figure 5. From this we can see thatthe proposed method is not sensitive to a change of sentence
content. Table 1 also shows results of the speaker-independent
models with/without the normalization. If the normalization
is not performed, the correlation coefficient decreases to 0.64.
This result shows that the prediction model is strongly affectedby the speaker differences. We can also observe this degradation in a scatter plot as shown in Figure 6. This degradation is
alleviated by using normalization as shown in Figure 7.
Table 2 shows correlation coefficients between eachprosodic variation parameter pairs. We can see that correlation coefficients tend to be low except for F0 and power distortion and their maximum values. Therefore, each of the otherprosodic variation parameters represents different property ofprosodic differences. Figure 8 shows changes of the correlation coefficient by adding the prosodic variation parameters to
the feature set one by one. The DTW distortion has a great contribution to the prediction for all speakers. By further adding
only the F0 distortion and the power distortion, the prediction
accuracy becomes almost equivalent to that achieved by using
all prosodic variation parameters.

3

3.5

4
4.5
Predicted distortion[dB]5

5.5

Figure 5: Scatter diagram of target mel-cepstral distortion andthat predicted by sentence-independent model (for Male 1).

These results suggest that 1) the mel-cepstral distortion canbe predicted using only three prosodic parameters (the DTW
distortion, the F0 distortion, and the power distortion) and 2)
the speaker- and sentence-independent prediction model can betrained using normalization of speaker differences in each parameter.

6. Conclusions
In this paper, we investigated intra-speaker spectral variation
between utterances of the same sentence. It was found thatlarger prosodic differences cause larger spectral variations, andacceptable spectral distortion in VC varies by prosodic variation. To predict the spectral variations caused by the prosodic
differences, we proposed a prediction method using a multiple
linear regression model to predict the mel-cepstral distortion
from several prosodic variation parameters. The experimental
results have demonstrated that 1) the mel-cepstral distortion ispredicted relatively well by the proposed method (the correlation coefficient is more than 0.7), 2) the prediction model isrobust against sentence differences, and 3) the prediction model
is sensitive to the speaker differences but this issue is well alleviated by the parameter normalization, and 4) good prediction
accuracy is achieved using only three prosodic parameters. We
plan to construct training, conversion, and evaluation metrics
considering the predicted spectral variation.

7. Acknowledgements
Part of this work was supported by JSPS KAKENHI Grant
Number 22680016.

93

T. Inukai, T. Toda, G. Neubig, S. Sakti, S. Nakamura
speaker 1
Correlation coefficient

Target distortion [dB]

5.5
5

4.5
4

0.8
speaker 3

0.75

averagespeaker 2

speaker 4

0.7

3.5

0.65 speaker 5
3
3

3.5

4
4.5
Predicted distortion [dB]5

DDTW Dpow

5.5

4

2
Target distortion

DU/V

Ddur

Dmax
Dmin
Dmin
Dmax
F
pow
F
pow
0

0

Figure 8: Correlation coefficient when adding prosodic variation parameters one-by-one.
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Figure 6: Scatter diagram of target mel-cepstral distortion andthat predicted by speaker-independent model (for all speakers).

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