Real-Time Vibration Control of An Electrolarynx
based on Statistical F0 Contour Prediction
Kou Tanaka1 , Tomoki Toda2 , Graham Neubig1 , and Satoshi Nakamura1
1

Graduate School of Information Science, Nara Institute of Science and Technology
8916-5 Takayama-cho, Ikoma, Nara, JAPAN
Email: {ko-t, neubig, s-nakamura}@is.naist.jp
2
Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, JAPAN
Email: tomoki@icts.nagoya-u.ac.jp
Abstract—An electrolarynx is a speaking aid device to artificially generate excitation sounds to help laryngectomees
produce electrolaryngeal (EL) speech. Although EL speech isquite intelligible, its naturalness significantly suffers from theunnatural fundamental frequency (F0 ) patterns of the mechanicalexcitation sounds. To make it possible to produce more naturallysounding EL speech, we have proposed a method to automaticallycontrol F0 patterns of the excitation sounds generated fromthe electrolarynx based on the statistical F0 prediction, which
predicts F0 patterns from the produced EL speech in real-time.
In our previous work, we have developed a prototype systemby implementing the proposed real-time prediction method inan actual, physical electrolarynx, and through the use of the
prototype system, we have found that improvements of thenaturalness of EL speech yielded by the prototype system tend
to be lower than that yielded by the batch-type prediction. In
this paper, we examine negative impacts caused by latency of
the real-time prediction on the F0 prediction accuracy, and to
alleviate them, we also propose two methods, 1) modeling ofsegmented continuous F0 (CF0 ) patterns and 2) prediction offorthcoming F0 values. The experimental results demonstratethat 1) the conventional real-time prediction method needs a large
delay to predict CF0 patterns and 2) the proposed methods havepositive impacts on the real-time prediction.

I. I NTRODUCTION
Production of electrolaryngeal (EL) speech is one of the
major alternative speaking methods for laryngectomees. EL
speech is produced using an electrolarynx, which is typicallyheld against the neck to mechanically generate artificial excitation signals. The generated excitation signals are conducted
into the speaker’s oral cavity, and are articulated to produce EL
speech. EL speech is relatively intelligible but its naturalness
is very low owing to unnatural fundamental frequency (F0 )patterns of the mechanically generated excitation signals.
To address this issue of EL speech, several methods havebeen proposed to control F0 patterns of the excitation signals
generated from an electrolarynx additionally using intentionally controllable signals, such as expiratory air pressure [1],up and down switched controlled by a finger [2], and forearm
movements [3]. Although these methods can change the F0
patterns, it is inherently difficult to control these signals togenerate natural F0 patterns corresponding to linguistic content
of the speech.
To generate more natural F0 patterns, we have proposeda method to control F0 values [4] based on statistical F0
prediction [5] [6] [7]. In this framework, F0 patterns are
predicted not according to signals consciously provided by
the speaker as in the other control methods but using only
the produced EL speech signals as shown in Fig. 1. Statisticalvoice conversion techniques [8] [9] have been successfully
applied to this prediction task. Relatively natural F0 patterns

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Fig. 1. Proposed system to directly control an electrolarynx using real-timestatistical F0 prediction for laryngectomees.

can be predicted using statistics extracted in advance fromparallel data consisting of utterance pairs of EL speech andnatural speech. Our preliminary experimental results througha simulation have demonstrated that the proposed method
yields significant improvements in naturalness while causing
no degradation in listenability and intelligibility compared tothe original EL speech [4][10].
In our previous work [11], we have developed a prototype
system by implementing our proposed F0 control method ofan electrolarynx based on statistical F0 prediction. We haveconfirmed that the naturalness of EL speech is significantly
improved by the use of our prototype system. On the other
hand, we have also found that improvements of the naturalness
of EL speech yielded by the prototype system tend to be lower
than that yielded by our conventional system based on batchtype prediction. It is possible that the real-time statistical F0
prediction requires a large delay time to ensure sufficient F0
prediction accuracy. Moreover, because the prototype system
also needs additional processing delay, its large delay time
significantly degrades naturalness of the enhanced EL speechin the form of causing larger mismatch between articulation
behavior and the predicted F0 patterns.
In this paper, we examine the effect of the delay time on
the real-time statistical F0 prediction. Moreover, to address thenegative impacts caused by latency of the real-time prediction
on the F0 prediction accuracy, we propose two methods:
1) modeling of segmented continuous F0 (CF0 ) patterns toshorten the required delay time in real-time statistical F0
prediction and 2) prediction of forthcoming F0 values tocancel the impact of the processing delay of the prototype
system. Through the experimental evaluation, we demonstratethat 1) the delay time required to predict CF0 patterns in theconventional prediction method can be significantly reduced
by using the segmented CF0 modeling strategy, and 2) the
negative impacts of the processing delay can be effectively

(Y |X)

alleviated by predicting the forthcoming F0 values.
II. D IRECT C ONTROL OF E LECTROLARYNX BASED ON
S TATISTICAL F0 P REDICTION [4]
A. Strategy of Direct Control of Electrolarynx
Our proposed system (shown in Fig. 1) to directly control
F0 patterns of the excitation signals generated from an electrolarynx consists of prediction and articulation processes. In theprediction process, the F0 value is predicted from EL speech
produced by a laryngectomee frame by frame using the realtime voice conversion algorithm. In the articulation process,the laryngectomee produces the EL speech by articulating
the excitation sounds generated from the electrolarynx basedon the predicted F0 values. Therefore, this system allows
laryngectomees to directly produce enhanced EL speech withmore natural F0 patterns corresponding to linguistic contents.
B. Statistical F0 Prediction Method
We briefly review our statistical F0 prediction method [5][6] [7], which exploits the idea of statistical voice conversion
techniques [8] [9]. The aim of this method is to predict F0 contours from the spectral parameters of EL speech. We describe
training, batch-type prediction, real-time prediction processes
and problems of delay arising in real-time processing.
In the training process, a parameter set λ of the joint⊤ ⊤probability density function P ([X ⊤t , Y t ] |λ) modeled by
a Gaussian mixture model (GMM) is trained [12] using aparallel data set consisting of utterance pairs of the EL speech
and target normal speech, where X t and Y t denote source
features and target features at time frame t, respectively, and ⊤denotes transposition. The corresponding joint feature vectors
can be obtained by performing automatic frame alignment
between the EL speech and the target normal speech withdynamic time warping. As the source features X t , the spectral
segment features of EL speech are extracted on a frame-byframe basis from the mel-cepstra at multiple frames aroundthe current frame t [13]. The target features Y t = [yt , ∆yt ]⊤consist of the static and delta (time derivative) componentsof the log-scaled F0 value yt , extracted on a frame-by-frame
basis from the target normal speech. Note that to improveprediction accuracy, we use continuous F0 (CF0 ) patterns
which interpolate unvoiced frames of F0 patterns by using
spline interpolation, and remove micro-prosody [14].
In the batch-type prediction process, given the spectral⊤ ⊤segment sequence X = [X ⊤of EL speech, the1 , . . . , XT ]most likely F0 sequence y = [y1 , . . . , yT ]⊤ can be obtained
as follows:ŷ = argmax P (Y |X, λ) subject to Y = W y , (1)y∑P (Y |X, λ)=P (Y |X, m, λ)P (m|X, λ)m≃ P (Y |X, m̂, λ)P (m̂|X, λ),m̂= argmax P (m|X, λ),

(2)
(3)

m
(Y |X)(Y |X)P (Y |X, m̂)= N (Y ; E m̂ , D m̂ )∏T
(Y |X)(Y |X)= t=1 N (Y t ; E m̂t ,t , D m̂t ),⊤ ⊤[Y ⊤1 ,...,Y T](4)

where Y =denotes the joint static anddynamic feature vector sequence, W is a linear transform
to append dynamic features into the static feature sequence,m = {m1 , . . . , mT } indicates a sequence of mixture indices,

E m̂t ,t is the conditional mean vector at frame t, which
is given by the mixture-dependent linear transformation of(Y |X)
the source feature vector X t , and D m̂t
is the conditional
covariance matrix depending of the mixture component m̂t .
The ML estimate of F0 sequence ŷ is analytically determined
as follows:(Y |X) −1ŷ = (W ⊤ D m̂(Y |X) −1

W )−1 W ⊤ D m̂(Y |X)

E m̂

.

(5)

The real-time prediction process is achieved by using a computationally efficient real-time voice conversion method [15]based on a low-delay conversion algorithm [16]. To approximate the batch-type prediction process with the framewise prediction process, we divide the F0 sequence y into
overlapped (L + 1)-dimensional segment vectors y (t) =[yt−L , . . . , yt ]⊤ at individual frames. With treating the segment vectors as a latent variable, the following linear dynamical system can be designed [17]:(y|X)(y|X) ⊤

y (t) = J y (t−1) + [01×L , µm̂t ,t + nm̂t(∆y|X)(∆y|X)µm̂t ,t = wy (t) + nm̂t
,

] ,

(6)
(7)

where the state transition matrix J just shifts the previous
segment vector y (t−1) , and the linear transform w to calculate
the dynamic features at frame t from the segment vector.
(∆y|X)(y|X)
The observation µm̂t ,t , a parameter µm̂t ,t , process noise(y|X)(∆y|X)
nm̂t , and observation noise nm̂t
are described with
(Y |X)
the conditional mean vector E m̂t ,t and covariance matrix(Y |X)
D m̂t
at frame t. The segment vector is recursively updated
frame by frame with Kalman filtering, and its first componentyt−L is used as the ML estimate ŷt−L . Therefore, the F0 value
at frame t is determined by considering all past frames, acurrent frame, and next L frames.
C. Effect of Delay Time on Real-time F0 Prediction Accuracy
In the previous work [16], it has been reported in a spectral
conversion task that the delay time depending on the segment
feature length L in the real-time prediction process requires
around 50 to 70 msec to maintain the conversion accuracyof the batch-type prediction process. On the other hand, no
previous work has examined the effect of the delay time on
the F0 prediction accuracy. It is possible that longer delay will
be required because F0 is a suprasegmental feature, which hasstrong correlation over a wider range compared to segmental
features, such as spectral parameters. This effect is expected
to be stronger in CF0 patterns.
III. D EVELOPMENT OF P ROTOTYPE S YSTEM
A. Implementation of Real-Time F0 Control
A prototype system based on our proposed system wasdeveloped using a laptop and a digital/analog (D/A) converter
shown in Table I. As shown in Fig. 1, EL speech produced
from mouth of a laryngectomee is detected with a usual
close-talk microphone. The EL speech signal is recorded ona laptop and F0 patterns of normal speech are predicted on
the fly by using the real-time statistical F0 prediction. The
predicted F0 values are linearly converted to voltage values
to control the F0 values of the excitation signal generated
by an electrolarynx. Then, an electric signal corresponding
to the determined voltage values is generated with the D/Aconverter connected from the laptop to the electrolarynx. The

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200 b)
100
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200 c)
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electrolarynx generates the excitation signal with the predicted
F0 values according to the input electric signal generated fromthe D/A converter.
As described in the previous section, the F0 patterns areconstantly delayed owing to latency of the real-time statistical
F0 prediction process. Moreover, additional latency is causedin the prototype system by the D/A converter. Figure 2 shows
latency caused by each step. In our implementation, 50 msec
latency is caused by the real-time statistical F0 prediction. For
the D/A part, it takes around 50 msec to write the digital signal
on the D/A converter, where the digital signal to be written
needs to be determined before starting writing. Consequently,
the D/A part causes 100 msec latency. In total, 150 msec
latency is caused in the prototype system1 .
B. Negative Impacts Caused by Latency
The F0 patterns predicted by the prototype system strongly
correlate to those by the simulated system [10], with acorrelation coefficient higher than 0.9. This high correlation
demonstrates that the proposed implementation is effective
and the simulated system is able to effectively approximate
the results of the prototype system. Through the use of
the prototype system, we confirmed that it yields significant
improvements in naturalness of EL speech while preserving its
high intelligibility as expected in the previous evaluation [10].
However, we also found that the naturalness of enhanced EL
speech tends to be lower than that yielded by the batch-type
prediction. We assume that this degradation is caused by 1)
the shorter delay time compared to the required delay timeon the real-time prediction of CF0 patterns, and 2) additional
processing delay of the prototype system. It is necessary tocompare the F0 patterns predicted by the real-time prediction
with those predicted by the batch-type prediction.

TABLE IE LECTRONIC DEVICES ON THE PROTOTYPE SYSTEMElectrolarynx
Microphone
CPU of the laptop
D/A converter

Yourtone
Crown CM-311AIntel(R) Core(TM) i5-4200U
AIO-160802AY-USB

1 Note that latency in the D/A part could be addressed by the developmentof a special device for the electrolarynx. Moreover, we have successfully implemented statistical VC processing on a digital signal processor (DSP) [18].
It is thus expected that all processors could be embedded into the electrolarynx
and total latency will be decreased to the 50 msec caused by the real-timestatistical F0 prediction.

50
0

1

2
Time [s]34Fig. 3. a) F0 patterns extracted from normal speech, b) smoothed continuous
F0 patterns interpolated at unvoiced frames, and c) segmented CF0 patternsof (b) extracted by using the power of waveform.

IV. P ROPOSED M ETHODS FOR A DDRESSING L ATENCYI SSUESOn our prototype system, the produced EL speech suffersfrom a misalignment between articulation and the constantly
delayed F0 patterns. To address this issue, we propose two
methods for reducing the delay time caused by the real-timeprocess while preserving F0 prediction accuracy at the levelof the batch-type prediction process.
A. Segmented Continuous F0 Patterns
In the previous CF0 modeling method, the prediction process given in Eq. (1) is performed utterance by utterance.
Because inter-frame correlation over an utterance is consideredin this process, a long delay is required in real-time prediction
to achieve sufficient prediction accuracy.
To reduce the delay time, we propose a segmented CF0
pattern modeling method to make the range of which we
consider inter-frame correlation shorter than an utterance.
Shorter segments are first extracted from each utterance, andthen, CF0 patterns of individual segments (i.e., segmented
CF0 patterns) are modeled and predicted separately. In thispaper, we determine the individual segments by extracting timeframes of which the waveform power is over a pre-determinedthreshold. An example of the segmented CF0 patterns is shownin Fig. 3. Note that the segmented CF0 patterns are still
different from the original F0 pattern, which is segmented byunvoiced frames, in that 1) the segmented CF0 patterns canalso include unvoiced frames, and thus they tend to be longer
than segments observed in the original F0 patterns, and 2)
each segmented CF0 pattern varied more smoothly than theoriginal F0 patterns.
B. Forthcoming F0 Prediction
In order to cancel the misalignment between articulationand the constantly delayed F0 patterns predicted in the realtime process, we investigate the possibility of predicting
forthcoming F0 values. We train the GMM for modeling the⊤⊤joint probability density P ([X ⊤t , Y t+F ] |λ) of the source
features at frame t, X t and the target features at frame t + F ,Y t+F . The trained GMM is used to predict the F0 value at F
frames ahead. For example, if the latency of the prototype
system is set to 200 msec, we train the GMM to predictthe F0 values at 200 msec ahead. Consequently, there is no
mismatch between articulation and the predicted F0 patterns.

B. Comparison of F0 Modeling Methods in Batch-type F0
Prediction
We evaluated the prediction accuracy of each F0 patternmodeling method in the batch-type process using the correlation coefficient between the predicted F0 pattern and the target
F0 pattern. The number of mixture components of the GMM
was optimized separately in each modeling method.
Table II shows the results as well as the number of mixture components optimized for each modeling method. CF0
yields a significant improvement in the prediction accuracy
compared to the original F0 as reported in [7]. The proposed
segmented CF0 modeling method (Seg CF0 ) preserves suchan improvement relatively well while minimizing degradationof the prediction accuracy.
C. Comparison of the Accuracy of Batch-type F0 Prediction
and Real-time F0 Prediction
As mentioned in Section IV-A, it is possible in the realtime prediction that the larger delay time is required in theCF0 pattern than in the F0 pattern to achieve the prediction

Correlation coefficients

V. E XPERIMENTAL E VALUATION
A. Experimental Conditions
We conducted four types of objective evaluation to examine
the performance of the developed prototype system and theeffectiveness of the proposed methods. The first evaluation is
a comparison of the prediction accuracy among three types ofF0 pattern modeling, F0 , CF0 , and the proposed segmental
CF0 (Seg CF0 ), in the batch-type prediction process. The
second evaluation is a comparison of the accuracy of batchtype F0 prediction and real-time F0 prediction. The third
evaluation is conducted to examine the effectiveness of the
proposed segmental CF0 modeling method in the real-time
prediction process. The last evaluation is conducted to examine
the effectiveness of the proposed forthcoming F0 prediction
method. The source speech was EL speech uttered by a malespeaker, and the target speech was normal speech uttered bya professional female speaker. Each speaker uttered about 50
sentences in the ATR phonetically balanced sentence set [19].
We conducted a 5-fold cross validation test in which 40
utterance pairs were used for training, and the remaining 10
utterance pairs were used for evaluation. Sampling frequency
was set to 16 kHz.
We employed FFT analysis with a 25 msec hanning window
to extract the mel-cepstra of EL speech as the spectral parameters. The frame shift length was set to 5 msec. As the source
features, the spectral segment features were extracted from themel-cepstra at the current ± 4 frames. On the other hand, F0
values of normal speech were extracted with STRAIGHT F0
analysis [20] and CF0 patterns were generated as the target
feature using a low-pass filter with 10 Hz cut-off frequency.
Moreover, the target F0 patterns were shifted so that theirmean value was equal to 100 Hz to predict F0 patterns suitable
for the source male speaker.

1

0.9

0.8
F0
CF0

0.7
50

100

150
200
250
Delay times [msec]

300

Fig. 4. Comparison of the accuracy of batch-type F0 prediction and real-time
F0 prediction.
0.6
Correlation coefficients

It is expected that there is a tradeoff between the prediction
accuracy and the setting of F ; i.e., larger F accepts a longer
delay time in the real-time prediction process, which makes thereal-time prediction accuracy close to the batch-type prediction
accuracy; on the other hand, it is obviously more difficult topredict F0 values at frames far away from the current one thanthose at closer frames.

0.5
0.4
0.3
0.2
F0
CF0
Seg CF0

0.1
0
50

100

150

200
250
Delay times [msec]

300

Fig. 5. Relationship of the delay time and each F0 pattern on real-time F0
prediction accuracy (w/ delay time correction at the time of evaluation).

accuracy comparable to that of the batch-type prediction. To
examine this possibility, we calculated a correlation coefficient
between the F0 pattern predicted by the real-time prediction
with various settings of the delay time and that by the batchtype prediction.
The result of a comparison between the CF0 and F0 patterns
is shown in Fig. 4. As for the F0 pattern, even if setting thedelay time to 60 msec (corresponding to L = 5), a quite
high correlation coefficient (around 0.89) is achieved. On theother hand, as for the CF0 pattern, the predicted patterns arequite different from those by the batch-type process, showingthat the correlation coefficient is less than 0.8 when setting
the delay time to less than 85 msec. Moreover, its accuracy
convergence is much slower compared to that observed in theF0 pattern. Consequently, in the CF0 pattern, the delay timeneeds to be set to around 250 msec to achieve the prediction
accuracy comparable to that of the batch-type prediction.
D. Evaluation of the Proposed Segmental CF0 Modeling
We evaluated the real-time prediction accuracy of each F0
modeling method using the correlation coefficient between thepredicted F0 pattern and the target F0 pattern. To evaluate only

TABLE II
ACCURACY OF BATCH - TYPE P REDICTION
The number of
mixture components
F0 correlation
coefficients

F0

CF0

Seg CF0

32

16

16

0.40

0.48

0.46

Correlation coefficients

0.6

have demonstrated that 1) the conventional real-time predictionneeds a large delay to predict CF0 patterns and 2) the proposed
methods have positive impacts on the real-time prediction.

0.5
0.4

ACKNOWLEDGMENT
This work was supported in part of JSPS KAKENHI Grant
Numbers: 15J10727 and 26280060, and the authors would liketo thank Mr. Sugai of Densei Communication Inc., Japan, foradvise to control an electrolarynx.

0.3
0.2
0.1

CF0
CF0 considered delay times

0
50

100

150

200
250
Delay times [msec]

300

Fig. 6. Comparison of basic CF0 patterns modeling and forthcoming CF0
patterns modeling (w/o delay time correction at the time of evaluation).

the prediction accuracy, the effect of the misalignment between
the predicted and the target F0 patterns, which is observedon the prototype system, was removed in this evaluation byshifting the predicted F0 patterns according to the delay time
settings in calculation of the correlation coefficient.
The results are shown in Fig. 5. As for the F0 pattern,although the prediction accuracy quickly converges at around60 msec of the delay time, the resulting correlation coefficient
is lower than 0.4 because the prediction accuracy of thebatch-type prediction is also low, as shown in Table II. As
for the CF0 pattern, the converged prediction accuracy issignificantly higher than that in the F0 pattern, as also observedin Table II, and its convergence is very slow. To achievesufficient prediction accuracy, the delay time needs to be
set to around 250 msec, as also observed in Fig. 4. On theother hand, the use of the proposed segmented CF0 patterns
makes the convergence faster than that of the CF0 patterns
while preserving its prediction accuracy. Consequently, theprediction accuracy comparable to the batch-type prediction
can be achieved by setting the delay time to around 150 msec.
E. Evaluation of the Proposed Forthcoming F0 Prediction
We evaluated the real-time prediction accuracy also considering the effect of the misalignment between articulationand the delayed F0 patterns predicted in the real-time process,which was observed in a practical situation, using the correlation coefficient between the predicted F0 pattern without any
correction of the delay time and the target F0 pattern.
The proposed forthcoming F0 prediction method was applied to the CF0 pattern and its effectiveness was examined.
The result is shown in Fig. 6. If not using the proposed
forthcoming F0 prediction, the delay time is set to longer,
the prediction accuracy gets lower. This result shows that theadverse effect of the misalignment on the actual prediction
accuracy is significantly large. This issue is well addressed byusing the proposed forthcoming F0 prediction. Consequently,
by setting the delay time to around 250 msec, the real-time prediction with the proposed forthcoming F0 prediction method
makes it possible to achieve prediction accuracy comparable
to that of the batch-type prediction.
VI. C ONCLUSION
In this paper, we have examined negative impacts caused bylatency of the real-time prediction on F0 prediction accuracy,
and to alleviate them, we have also proposed two methods, 1)
modeling of segmented continuous F0 (CF0 ) patterns and 2)
prediction of forthcoming F0 values. The experimental results

R EFERENCES[1] N. Uemi, T. Ifukube, M. Takahashi, and J. Matsushima, “Design of a
new electrolarynx having a pitch control function,” in Proc. 3rd IEEE
International Workshop of Robot and Human Communication, pp. 198–
203, Jul. 1994.
[2] Y. Kikuchi and H. Kasuya, “Development and evaluation of pitch
adjustable electrolarynx,” in Proc. Speech Prosody 2004, International
Conference., pp. 761–764, Mar. 2004.
[3] K. Matsui, K. Kimura, Y. Nakatoh, and Y. O. Kato, “Development ofelectrolarynx with hands-free prosody control,” in Proc. SSW8, pp. 273–
277 , Aug. 2013.
[4] K. Tanaka, T. Toda, G. Neubig, S. Sakti, and S. Nakamura, “Direct F0control of an electrolarynx based on statistical excitation feature prediction and its evaluation through simulation,” in Proc. INTERSPEECH,
Sep. 2014.
[5] K. Nakamura, T. Toda, H. Saruwatari, and K. Shikano, “Speakingaid systems using GMM-based voice conversion for electrolaryngeal
speech,” in Speech Communication, vol. 54, no. 1, pp. 134 –146, Jan.
2012.
[6] H. Doi, T. Toda, K. Nakamura, H. Saruwatari, and K. Shikano, “A laryngeal speech enhancement based on one-to-many eigenvoice conversion,”
Audio, Speech, and Language Processing, IEEE/ACM Transactions on,
vol. 22, no. 1, pp. 172–183, Jan. 2014.
[7] K. Tanaka, T. Toda, G. Neubig, S. Sakti, and S. Nakamura, “A hybrid approach to electrolaryngeal speech enhancement based on noise
reduction and statistical excitation generation,” IEICE Transactions on
Information and Systems, Jun. 2014.
[8] Y. Stylianou, O. Cappe, and E. Moulines, “Continuous probabilistictransform for voice conversion,” Speech and Audio Processing, IEEE
Transactions on, vol. 6, no. 2, pp. 131–142, Mar. 1998.
[9] T. Toda, A. Black, and K. Tokuda, “Voice conversion based on
maximum-likelihood estimation of spectral parameter trajectory,” Audio,
Speech, and Language Processing, IEEE Transactions on, vol. 15, no. 8,
pp. 2222–2235, Nov. 2007.
[10] K. Tanaka, T. Toda, G. Neubig, S. Sakti, and S. Nakamura, “An interspeaker evaluation through simulation of electrolarynx control based on
statistical F0 prediction,” Proc. APSIPA ASC, Dec. 2014.
[11] K. Tanaka, T. Toda, G. Neubig, S. Sakti, and S. Nakamura, “An enhanced
electrolarynx with automatic fundamental frequency control based on
statistical prediction,” Proc. ASSETS, pp. 435–436, Sep. 2015.
[12] A. Kain and M. Macon, “Spectral voice conversion for text-to-speechsynthesis,” in Proc. ICASSP, vol. 1, pp. 285–288, May. 1998.
[13] T. Toda, M. Nakagiri, and K. Shikano, “Statistical voice conversion
techniques for body-conducted unvoiced speech enhancement,” Audio,
Speech, and Language Processing, IEEE Transactions on, vol. 20, no. 9,
pp. 2505–2517, Nov. 2012.
[14] K. J. Kohler, “Macro and micro F0 in the synthesis of intonation,”
Papers in Laboratory Phonology I, pp. 115–138, 1990.
[15] T. Toda, T. Muramatsu, and H. Banno, “Implementation of computationally efficient real-time voice conversion.” in Proc. INTERSPEECH,
Sep. 2012.
[16] T. Muramatsu, Y. Ohtani, T. Toda, H. Saruwatari, and K. Shikano, “Lowdelay voice conversion based on maximum likelihood estimation ofspectral parameter trajectory,” in Proc. INTERSPEECH, pp. 1076–1079,
Sep. 2008.
[17] T. Toda, “Augmented speech production based on real-time statisticalvoice conversion,” Proc. GlobalSIP, pp. 755–759, Dec. 2014.
[18] T. Moriguchi, T. Toda, M. Sano, H. Sato, G. Neubig, S. Sakti,and S. Nakamura, “A Digital Signal Processor Implementation of
Silent/Electrolaryngeal Speech Enhancement based on Real-Time Statistical Voice Conversion,” in Proc. INTERSPEECH, pp. 3072–3076, Aug.
2013.
[19] M. Abe, Y. Sagisaka, T. Umeda, and H. Kuwabara, “Speech database,”
ATR Technical Report, TR-I-0166, Sep. 1990.
[20] H. Kawahara, I. Masuda-Katsuse, and A. de Cheveigné, “Restructuring
speech representations using a pitch-adaptive time–frequency smoothing
and an instantaneous-frequency-based F0 extraction: Possible role of arepetitive structure in sounds,” in Speech Communication, vol. 27, no. 3.
Elsevier, pp. 187–207, Apr. 1999.