PROSODY-CONTROLLABLE HMM-BASED SPEECH SYNTHESISUSING SPEECH INPUT
Yuri Nishigaki, Shinnosuke Takamichi, Tomoki Toda, Graham Neubig, Sakriani Sakti, Satoshi Nakamura
Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Japan
Email: {shinnosuke-t, tomoki}@is.naist.jp
ABSTRACT

Input speech

Input text

This paper proposes an HMM-based speech synthesis system thatmakes it possible to control the prosody of the synthesized speechthrough speech input. As creative activities using speech synthesis technologies have been rapidly growing in popularity, there isgreat demand for interfaces to synthesize speech of a specific target
speaker as the users want. The proposed system allows the users
to guide prosody of synthetic speech of the target speaker by using
their own speech while preserving the original functionality of the
HMM-based speech synthesis as a text-to-speech synthesis system.
The proposed system consists of 3 main modules: a duration determination module, a F0 modification module, and a speech parameter generation module. The first 2 modules ensure that the duration
and F0 of the input speech are reflected in the synthetic speech, andthe last module generates synthetic speech parameters according to
the determined duration. We examine properties of each module on
speech quality and prosodic mimicking ability of synthetic speech,with experimental resulting demonstrate the effectiveness of the proposed system.

Speech analysis

Text analysis

Index Terms— HMM-based speech synthesis, prosody modification, duration unit, model adaptation, unvoiced/voiced information,
1. INTRODUCTION
Various techniques of corpus-based speech synthesis have achieved
remarkable improvements in terms of speech quality and reproduction accuracy of speaker individuality in synthetic speech [1, 2, 3].
Due to this innovation, there is now a high demand for systems tosynthesize speech or singing voices of a specific target speaker, e.g.,
text-to-speech synthesis system CeVIO [4] and singing voice synthesis system VOCALOID [5]. Such systems are being more andmore widely utilized for speech-based creative activities.
Text-to-speech is a fundamental technique for speech-based creative activity. Especially, HMM-based speech synthesis [6, 7] is usedbecause this makes it possible to flexibly control the voice characteristics and speaking style of synthetic speech. Compared to systems
that manually control speech features [8], HMM-based speech synthesis allows for higher controllability through controlling the model
parameters [9]. On the other hand, this sort of flexible control is noteasy for novice users because it is still difficult to control the voice
characteristics as the users want.
In this paper, we consider the use of speech information utteredby the users as supportive information for manual control. For example, [10] proposed a method for automatic parameter optimization of VOCALOID referring the user’s input singing voice. Thissystem allows the users to reflect the voice characteristics of their
input singing voice on the synthesized singing voice.

Spec., F0, & apf. of input speech
F0 of
Input speechContextual feature of
input text

Dur. determination
moduleState alignment

Dur. of input speech

Duration generation

F0 modification
moduleAlignment HMM
Dur. model
Synthesis HMM

Dur. ofsynthetic speech

Pitch conversion
Converted F0

U/V modification

Reference F0

F0 of synthetic speech

Parameter generation

Parameter generation
Spec. & apf. of synthetic speech
moduleWaveform generation
Synthetic speechFig. 1. An overview of the proposed system. Spec., apf., and dur.
indicate spectral parameters, aperiodicity, and duration, respectively.

As a related work in speech synthesis, voice conversion techniques [11] can be utilized to convert users’ input voices into the target speaker’s voice. Because voice conversion can preserve the input
prosody, such a system can synthesize the target speaker’s voice withany variety of prosody. However, speech quality and speaker individuality in speech created by voice conversion tend to be worse thanspeech created by HMM-based speech synthesis. To address thisproblem, [12] proposed a system to reflect input prosody utilizing
the HMM-based speech synthesis framework. Although this system
makes it possible to control the prosody by input speech while preserving the quality of HMM-based speech synthesis, it is not guaranteed that this system works similarly to standard HMM-based speechsynthesis systems because this system needs to use contextual features including the input prosody information. We assume that we
can create an easier-to-control system if both prosody control by input speech and the original functions of HMM-based speech synthesis are available, which means that input speech-based and manualcontrol are both available.
In this paper, we propose an HMM-based speech synthesis system with prosody modification based on speech input that preserves
the original functions of HMM-based speech synthesis. The proposed system shown in Fig. 1 consists of 3 main modules: (1) aduration determination module to determine duration of synthetic
speech using HMM-state alignments, (2) an F0 modification module to generate the F0 contour by modifying that of the input speech,and (3) a speech parameter generation module to generate spectral
parameters and aperiodicity of synthetic speech. The first 2 modules
ensure that the duration and F0 of the input speech to are reflectedon the synthetic speech, and standard HMM-based speech synthe-sis is also available by using only the last module. This paper investigates the influence of each module on the speech quality andprosody mimicking ability of synthetic speech. The experimental
results demonstrate the effectiveness of the proposed system.

‘k’

‘a’
Alignment HMMs
Input speech parameters

2. HMM-BASED SPEECH SYNTHESISIn the training stage, natural speech parameters (spectral parameters, the F0 contour, and aperiodicity) sequences are modeledwith context-dependent HMMs. Introducing HMM-state duration
models, these speech parameters and the HMM-state duration aremodeled in a unified training framework [13]. In the synthesis
stage, HMMs corresponding to input text are constructed from thetrained context-dependent HMMs, and the HMM-state duration
q = [q1 , · · · , qt , · · · , qT ] is determined by maximizing the duration
likelihood as follows:q̂ = argmax P (q|λ) ,(1)qwhere T is a total number of frames, qt is a HMM-state index at
frame t, and λ indicates the parameter sets of the HMMs. The
speech parameter sequence of synthetic speech is generated by maximizing HMM likelihood under the explicit constraint between staticand dynamic features [14] as follows:ŷ = argmax P (W y|q̂, λ) ,
(2)
y



>
> >
where y = y >is a speech parameter se1 , · · · , yt , · · · , yT
quence. y t = [y1 , · · · , yd , · · · , yD ]> is a D-dimensional speechfeature vector at frame t, W is the weighting matrix for calculating
the dynamic features [7]. The synthetic speech waveform is synthesized by a vocoding process using the generated speech parameters.
Although the speech quality in synthetic speech tends to be worse
than to natural speech, the speech quality is improved by considering global variance in the parameter generation [15].
3. PROSODY-CONTROLLABLE HMM-BASED SPEECHSYNTHESIS USING SPEECH INPUTThis section describes how the proposed system controls the prosodyof synthetic speech. Here, we call a person who utters input speechas the input speaker, and also call the HMMs used for state alignment and synthesizing waveform (i.e., the target speaker’s HMMs)
as the alignment HMMs and the synthesis HMMs, respectively.
3.1. Duration Determination Module
In the duration determination module, we first construct sentence
HMMs corresponding to the input text using the alignment HMM,
then perform HMM-state alignment to extract the state duration of
the input speech. The state duration of synthetic speech is determined given the duration of input speech. In this paper, we address
the 3 following problems of this module.
3.1.1. Duration Generation Using HMM-state Alignments of Input
SpeechThis module requires alignment HMMs that accurately model input speech features for robust alignment. We suppose two cases
using the input speaker’s pre-recorded speech: (1) input speakerdependent HMMs can be trained from a large amount of the input
speaker’s pre-recorded speech, and (2) the target speaker’s HMMs
are adapted [16] using a small amount of input speaker’s speech.
Note that a speaker-independent or an average-voice model [16]could be adopted.

Synthesis HMMs
(given state duration)
Synthetic speech parameters
Synthesis HMMs
(given phoneme duration)
‘k’

‘a’

Fig. 2. An example of state or phoneme-level duration mappedfrom input speech to synthetic speech. HMM-state duration is redistributed by the duration models of the synthesis HMMs when thephoneme duration is given.

3.1.2. Better Duration Units
Because the alignment and synthesis HMMs are trained using otherspeakers’ speech features, it is not guaranteed that their HMM-statesmodel the corresponding speech segment1 . Therefore, state-level
duration mapping is expected to deteriorate the speech quality ofsynthetic speech. In this paper, we consider 3 types of the duration
unit mapped from the input speech to the synthetic speech: state,phoneme, and mora duration. When the phoneme or mora duration
is used, the state duration of synthetic speech is determined by maximizing the duration likelihood given the duration of each phoneme
as shown in Fig. 2.
3.1.3. Robustness to Various Speaking Style of Input Speech
As we described in Section 1, this system is expected to be used forspeech-based creative activities. In such a case, voice characteristics
of input speech vary widely, and are not always the same as thoseof the target speaker’s speech used for training the synthesis HMMs.
The mismatch between voice characteristics causes a degradation in
accuracy. In order to address this problem, we adapt the alignment
HMMs using input speech features. The alignment process is performed utterance by utterance.
3.2. F0 Modification ModuleThis module generates an F0 contour of synthetic speech using the
input speaker’s F0 contour with a reference F0 contour generated
from the target speaker’s HMMs (synthesis HMMs) in the parameter
generation module.
3.2.1. Pitch Conversion
To correct the F0 range difference between input speaker’s and target speaker’s F0 contours, the input speaker’s F0 contour is linearly
converted as follows:σy(xt − µx ) + µy ,
(3)
x̂t =
σx
1 For example, a central sub-segment of a speech segment of one phoneme
is assumed to be modeled with the 2nd HMM-state of the alignment HMMs,
but there is no guarantee that it is modeled with the same HMM-state of thesynthesis HMMs.

4. EXPERIMENTAL EVALUATION

300
200

Input speech

4.1. Experimental Setup

F0 [Hz]

100
300
200
Reference generated
from targer speaker's HMM

100

After pitch conversion

300
200

After pitch conversion & U/V modification

Synthetic
speech

100
250

300

350

400
450
Frame index

500

550

600

Fig. 3. An example of F0 contours of input speech, reference generated from target speaker’s HMMs, and synthetic speech.

where xt is the log-scaled F0 value of input speech at frame t, µx
and σx are mean and standard deviation of xt , µy and σy are thoseof the target speaker, respectively. These statistics are calculated
utterance by utterance.

3.2.2. Unvoiced/Voiced Region Modification
Because the input speaker’s and target speaker’s reference F0 contour have the unvoiced/voiced regions related to their own spectral
parameters, we expect speech quality of synthetic speech to sufferfrom mismatch between the input speaker’s U/V region and the target speaker’s spectral parameters2 . Therefore, we perform U/V region modification after the pitch conversion process. An example isshown in Fig. 3. A continuous F0 contour [17, 18] is first estimatedby spline-based interpolation using the F0 contour of input speech.
Micro prosody [19] is removed during this process. The finally determined F0 contour is generated by restoring the U/V region of thereference F0 contour.

3.3. Speech Parameter Generation ModuleThis module generates the spectral parameters, F0 contour, and aperiodicity given the determined duration. The generated F0 is used
for the F0 modification module as described. The synthetic speech
is synthesized using the spectral parameters and aperiodicity of thismodule and the F0 contour of F0 modification module.
Power is an essential component to reflect emphasis of input
speech on synthetic speech. The straightforward approach is to replace the generated power with that of the input speech, but there is atrade-off between the degree to which is reflected and unnatural emphasis caused by U/V modification described in the F0 modification
module. Therefore, we perform Maximum APosterior (MAP) adaptation [20] of synthesis HMMs using input speech parameters. The
degree of power reflection can be controlled by the hyper parameterof the MAP adaptation. For example, setting the hyper parameter to
0 effectively reflects the power of input speech but causes unnaturalemphasis.
2 Especially, speech quality is strongly degraded when spectral parameters
at voiced frame is excited by unvoicing noise signals.

We trained a synthesis Hidden Semi-Markov Model (HSMM) [21]for an Japanese female speaker as the target speaker. We used 450
sentences from subset A-through-I of ATR phoneme-balanced sentences [22] for training. Speech signals were sampled at 16 kHz. The
shift length was set to 5 ms. The 0th-through-24th mel-cepstral coefficients were extracted as spectral parameters and log-scaled F0 and
5 band-aperiodicity [23, 24] were extracted as excitation parameters.
The STRAIGHT analysis-synthesis system [25] was employed forparameter extraction and waveform generation. The feature vector
consisted of spectral and excitation parameters and their delta anddelta-delta features. 5-state left-to-right HSMMs were used. 2 maleand 2 female speakers different from the target speaker were used for
input speakers. The input-speaker dependent HMMs for alignments
were trained using 450 sentences from subset A-through-I of ATR
phoneme-balanced sentences uttered by each input speaker.

4.2. Evaluation of Duration Determination Module
4.2.1. Evaluation for Alignment Accuracy and Duration Unit
We investigate effects of the duration unit and adaptation of alignment HMMs. We prepared HMM-state (“state”), phoneme (“phone”),
and mora (“mora”) duration as the duration unit, and also prepared
input-speaker-dependent HMMs (“Ref.”), HMMs adapted using 1
utterance (“1 utt.”) or 56 utterances (“56 utts.”) uttered in advanceby input speaker. Additionally, the target speaker’s HMMs wereused to test accuracy when HMMs were not adapted (“Target”). We
conducted a MOS test on speech quality of synthetic speech and a
DMOS test on prosodic mimicking ability, using combinations of
3 duration units and 4 alignment HMMs (a total of 12 combinations). In the MOS test, the synthetic speech of each combination
was presented to listeners in random order. In the DMOS test, input
speech was first presented as a reference, then synthetic speech waspresented. 10 listeners rated the speech quality using a 5-point scalein the MOS test. Similarly, 14 listeners rated to what extent the
prosody of input speech is reflected on synthetic speech using a
5-point scale in the DMOS test. The 53 sentences uttered by each
input speaker was used for the evaluation data. U/V modification
was performed in the F0 modification module, but power adaptation
was not performed in the parameter generation module.
The results are shown in Figures 4 and 5. Comparing scores
between the adapted HMMs (“1 utt.” and “56 utts.”) and the nonadapted HMM (“Target”), the scores of the adapted HMMs achieved
better score, and “56 utts.” has a similar score to “Ref.” on speech
quality and prosodic mimicking ability, excepting when using mora
duration (“mora”) in the DMOS test. These results demonstrate that
adaptation of the alignment HMMs is effective on both speech quality and prosodic mimicking ability.
Mora duration tends to deteriorate the prosodic mimicking ability compared to other duration units. This is because the HMMstate duration of synthetic speech is strongly affected by the duration
models of synthesis HMMs when the mora duration is used.
Additionally, we conducted a t-test between the best score(“Ref.” HMMs with phoneme duration) and “1 utt.” with each
duration unit. We confirmed that there is a significant difference atthe 1% level when state duration and mora duration were used on
mimicking ability evaluation, and state duration was used on speech
quality, suggesting that phoneme duration is the most effective.

Fig. 5. Degradation mean scoresFig. 4. Mean opinion scoreson prosodic mimicking ability ofon speech quality of synthetic
synthetic speech to investigate
speech to investigate the effect of
the effect of the duration unit andthe duration unit and alignmentalignment HMM adaptation. (ErHMM adaptation. (Error bars inror bar indicate 95% confidence
dicate 95% confidence intervals.)intervals.)

Degradation mean
opinion score

5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0

Ref.

1 utt.(Ref.)

1 utt.(Target)

Fig. 6. Degradation mean opinion scores with 95% confidence withvarious speaking style of input speech.

4.2.2. Evaluation of Robustness to Various Speaking Styles of Input
SpeechWe prepared another evaluation data to investigate effectivenesswhen the input speaker uses various, possibly extreme, speaking
styles. We first extracted 10 famous phrases of Japanese television
dramas and animation, and then recorded input speaker’s voices,
asking them to imitate the extracted phrases. As the alignment
HMMs, input speaker-dependent HMMs (“Ref.”) trained using
speaking speech and “Ref.” HMMs and “Target” HMMs adaptedusing the input speech were used. 8 listeners participated in a
DMOS test on prosodic mimicking ability. U/V modification was
performed in the F0 modification module, but power adaptation was
not performed in the parameter generation module.
Figure 6 shows the result. The adapted HMMs have a betterscore than the speaker-dependent HMMs trained using regularlyspoken speech. This result demonstrates that adaptation using input
speech is effective to improve robustness to various speaking styleof input speech. “1 utt. (Ref.)” and “1 utt. (Target)” use the different
HMMs to be adapted, but there is no significant difference on mimicking ability. Therefore, we can find this adaptation process works
robustly with the HMMs to be adapted.
4.3. Evaluation of F0 Modification Module
Next, we investigated effectiveness of the U/V modification in theF0 modification module. We conducted a preference AB test on

95% confidence interval

10090Preference score [%]

80
70
60
50
40
30
20
10
0 w/o mod w/ mod w/o mod w/ mod w/o mod w/ mod w/o mod w/ mod
Speaker 1

Speaker 2

Speaker 3

Speaker 4

Fig. 7. Preference scores on speech quality between F0 contours
before and after modifying unvoiced/voiced region.
20
U/V modification rate [%]

5.0
state
4.5
phone
4.0
mora
3.5
3.0
2.5
2.0
1.5
1.0 Target 1 56 Ref.
utt. utts.

Degradation mean opinion score

Mean opinion score

5.0
state
4.5
phone
4.0
mora
3.5
3.0
2.5
2.0
1.5
1.0 Target 1 56 Ref.
utt. utts.

Voiced frame -> Unvoiced frame
Unvoiced frame -> Voiced frame

15
10
5
0

speaker1
speaker2 speaker3 speaker4
Fig. 8. U/V modification rates between F0 contours before and after
modifying the U/V region.

speech quality, using F0 contours with and without U/V modification (“w/ mod” and “w/o mod”). The alignment HMMs and the duration unit were input-speaker-dependent HMMs and phoneme duration, respectively. Moreover, we calculated the U/V modification
ratio before and after U/V modification.
Figures 7 and 8 show the result for speech quality and U/V modification rate, respectively. We can see that the U/V modificationachieves quality improvements for 3 input speakers. Moreover, we
can find the largest improvements for speaker 2, likely due to thefact that speaker 2 had the largest ratio modifying unvoiced frames
to voiced frames. Because exciting spectral parameters at the voiced
frame by unvoicing noise signals causes significant quality degradation as described in Section 3, the significant improvements forspeaker 2 are natural.
4.4. Evaluation of Speech Parameter Generation Module
Finally, we investigated the effectiveness of power adaptation of thesynthesis HMMs. The 10 phrases in Section 4.2.2 were used forevaluation. The hyper parameter was set to 1.0 from the preliminary evaluation. We conducted a preference XAB test on prosodic
mimicking ability, using the adapted HMMs (“w/ power”) and nonadapted HMMs (“w/o power”). Before the adaptation, we replaced
the 0th mel-cepstral coefficient with the log-scaled power and retrained the synthesis HMMs. Input speech was first presented, thenPreference score [%]100
80

95% confidence interval

60
40
20
0

w/ power
w/o power
Fig. 9. Preference scores on prosodic mimicking ability to confirm
the effectiveness of the power component adaptation.

pairs of synthetic speech sample was presented. The listener selected
a better sample in term of prosody mimicking ability. As shown in
Fig. 9, we can confirm the effectiveness of power adaptation, withthe adapted HMMs achieving a better score than the non-adapted
HMMs.
5. CONCLUSION
This paper has proposed HMM-based speech synthesis with prosody
modification based on speech input. The proposed system consistsof the duration determination, F0 modification, and speech parameter generation module. The experimental results have demonstrated
the properties of each module on naturalness and prosody mimicking ability of synthetic speech. As future work, we will try HMM
selection considering speech input.
Acknowledgements: Part of this work was supported by JSPS
KAKENHI Grant Number 26280060 and 2430073.
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