INTERSPEECH 2013

Improvements to HMM-Based Speech Synthesis
Based on Parameter Generation with Rich Context Models
Shinnosuke Takamichi† , Tomoki Toda† , Yoshinori Shiga‡ ,Sakriani Sakti† , Graham Neubig† and Satoshi Nakamura††

Graduate School of Information Science, Nara Institute of Science and Technology,
8916–5 Takayama-cho, Ikoma, Nara, 630-0192, Japan‡
National Institute of Information and Communications Technology,
3–5 Hikari–dai, Seika–cho, Soraku–gun, Kyoto, 619–0289, Japan.
Abstract

synthesis, the joint probability distribution of all speech components corresponding to one waveform segment is selected.
However, this method still loses the flexible control due to necessity of using a strong constraint among different speech components in synthesis.
As a hybrid method that preserves the flexibility of HMMbased speech synthesis, we have proposed a parameter generation method using rich context models [13]. The probability
distributions corresponding to individual waveform segments
are reformulated as GMMs separately for each speech component. A speech parameter trajectory at each component is generated based on the ML criterion using an iterative process. This
generation method has been applied to the spectral component.
However, while quality improvements in synthetic speech havebeen confirmed, the synthetic speech still sounds muffled. Because the parameter sequence generated by the iterative generation process strongly depends on the initial parameter sequence,
it can be expected that a setting of a suitable initial parametersequence will yield further improvements in the quality of synthetic speech. As another approach for quality improvements,
a F0 parameter generation method with rich context models is
expected. For F0 component, a statistical model using rich context models and the parameter generation method need to consider voiced/unvoiced region. It can also be expected for the F0
component that the setting of the initial parameter sequence willaffect quality improvements.
In this paper, we propose a technique to properly initialize parameters so that the parameter generation method using
rich context models produces higher-quality speech. A lesssmoothed but highly discontinuous parameter sequence is generated as an initial parameter sequence from probability distributions over-fitted to individual segments. We experimentally show that the use of this initial parameter sequence yields
significant quality improvements of synthetic speech. Moreover, we propose an F0 parameter generation method using richcontext models based on the Multi-Space Distribution HMM(MSD-HMM) [14]. The experimental results demonstrate thatfurther quality improvements are achieved by applying the proposed parameter generation method to both spectral and F0
components.

In this paper, we improve parameter generation with rich context models by modifying an initialization method and further
apply it to both spectral and F0 components in HMM-based
speech synthesis. To alleviate over-smoothing effects causedby the traditional parameter generation methods, we have previously proposed an iterative parameter generation method withrich context models. It has been reported that this method yields
quality improvements in synthetic speech but there are still limitations. This is because 1) this generation method still suffersfrom the over-smoothing effect, as it uses the parameters generated by the traditional method as an initial parameters, whichstrongly affect on the finally generated parameters and 2) it isapplied to only the spectral component. To address these issues, we propose 1) an initialization method to generate lesssmoothed but more discontinuous initial parameters that tend
to yield better generated parameters, and 2) a parameter generation method with rich context models for the F0 component.
Experimental results show that the proposed methods yield significant improvements in quality of synthetic speech.
Index Terms: HMM-based speech synthesis, rich context models, GMM, context clustering, over-smoothing, MSD-HMM

1. Introduction
The corpus-based approach [1] to Text-To-Speech (TTS) is
currently the most popular, and has two main synthesis techniques: sample-based synthesis such as unit selection synthesis [2, 3], and statistical parametric synthesis such as Hidden
Markov Model (HMM)-based speech synthesis [4]. In unit selection synthesis, although high-quality speech is synthesized
by the direct use of waveform segments [5], voice characteristics of the generated speech are fully dependent on the original voice. On the other hand, HMM-based speech synthesis uses well-formulated statistical parametric representation of
speech parameters. Although many merit such as a flexible control of the voice characteristics [6, 7, 8] is yielded, the generated speech parameters tend to be over-smoothed, and synthetic
speech sounds muffled compared with natural speech [9].
To alleviate this over-smoothing effect, some hybrid methods that lie between those two methods have been proposed[10, 11, 12]. Maximum likelihood (ML)-based unit selection synthesis [10] uses waveform segments retrieved from the
speech corpus to maximize the HMM likelihood. However,the use of waveform segments makes it impossible to flexibly
control voice characteristics of synthetic speech. As anotherhybrid method using the formulated parametric representation,rich context modeling that represents each waveform segmentwith a probability distribution of individual speech component
parameters such as spectrum and F0 has been proposed [11]. In

Copyright © 2013 ISCA

2. HMM-based Speech Synthesis
In HMM-based speech synthesis, various contextual factors areused to capture both segmental and prosodic features. Since
combinations of the contextual factors increase exponentiallyand the number of them is enormous, one context label (calleda ”full context”) usually corresponds to only one acoustic segment in the training data.
To robustly train context-dependent HMMs, different full364

25- 29 August 2013, Lyon, FranceDecision tree forgenerated parameters

context labels are tied together in a decision tree [15] constructed based on the Minimum Description Length (MDL) criterion [16], which is given byl(C) =1Γ (c) log |Σc | + aCD log Γ (0) ,2 c=1

Decision tree for
initial parameters

C

(1)

Clus

Clustered model

where c is a leaf node index, C is the total number of leaf nodes,
a is a parameter to control C, D is the number of feature dimensions, Σc is the covariance matrix of leaf node c, and Γ (c) andΓ (0) are state occupancy counts in leaf node c and the root
node, respectively. The output probability density function bc
is calculated in each leaf node. Different decision trees are constructed for individual speech components [15].
Spectral component: Spectral parameters are modeled bya continuous HMM. Its state output probability is given bybc (ot ) = N (ot ; μc , Σc ) ,
(2)



 
where ot = cis a feature vector includingt , Δct , ΔΔct
static features ct and dynamic features Δct ɼΔΔct , and μc is
the mean vector in the c-th leaf node. The Gaussian distribution
with mean vector μc and covariance matrix Σc is denoted as
N (·; μc , Σc ).
F0 component: F0 is modeled by an MSD-HMM [14].
Its state output probability is given by
wc N (ot ; μc , Σc ) , lt = V
bc (ot ) =,(3)
1 − wc ,lt = URich context model
GMM
Parameter generation

Proposed

Figure 1: An overview of the proposed initialization method.
where ωm is the mixture component weight of the m-th richcontext model (ωm = 1/Mc ), and the total number of mixture
components is Mc .
3.2. Parameter Generation Method [13]In synthesis, the parameter trajectory is generated to maximize
HMM likelihood. Given a state sequence q = [q1 , · · · , qT ] ,which is determined in the traditional way [15], we approximate
the HMM likelihood with a single mixture component sequencem̂ = [m1 , · · · , mT ] as follows:P (o, m|q, λ)  P (o, m̂|q, λ) .
(6)
all m
After setting the initial static feature vector sequence c(0) toa parameter sequence generated from the clustered models in
the traditional manner, the single mixture component sequenceand the static feature vector sequence are iteratively updated asfollows :m̂(i+1) = argmax P m|W ĉ(i) , q, λ ,(7)mĉ(i+1) = argmax P W c|m̂(i+1) , q, λ .
(8)where lt is a discrete voicing label that is either voiced V or
unvoiced U at frame t, and wc is the weight of the voiced spaceof leaf node c. Note that lt is observable together with ot .
In synthesis, full context labels to be synthesized are clustered with the decision trees and the output probability density functions at corresponding leaf nodes are selected to form
a sentence HMM. Then, a time sequence of the static fea 
is generated by maximizture vectors c = c
1 , · · · , cT
ing the HMM likelihood under a constraint on the relationshipbetween static and dynamic features (o = W c, where W is
the weighting matrix for calculating the dynamic features) [17].
While this method has many advantages in terms of flexibility,
it has the well known problem that over-smoothing of the generated speech parameters causes significant degradation in speech
quality.

c

One rich context model usually corresponds to one HMMstate acoustic segment. The model selection process (Eq. (7)) issimilar to selecting a single acoustic segment sequence to generate speech parameters. The likelihoods for both static and dynamic features used in this selection could be regarded as target
and concatenation costs in unit selection [18, 19].
3.3. Effect of Initial Parameter SequenceThis iterative parameter generation process easily falls into local optima, and thus the generated speech parameter sequencestrongly depends on the initial parameter sequence. In the previous work, the initial parameter sequence was generated byconventional clustered models. Initial parameters generated inthis way are continuous but excessively smoothed, resulting inover-smoothing even in the finally generated speech parameter sequence. Consequently, the resulting synthetic speech stillsounds muffled. On the other hand, if we are able to provide amore effective initialization, it will greatly improve the overall
speech quality.

3. Parameter Generation Methodwith Rich Context Models
3.1. Formulation of GMM Using Rich Context Models
Rich context models provide one way to alleviate the oversmoothing effect while preserving robustness of parameter estimation. In the rich context models, a mean vector is trainedfor each full context label and a covariance matrix is tied overdifferent full context labels belonging to each leaf node of thedecision tree [11]. The output probability density function (richcontext model) of the continuous HMM for the m-th full context label in the c-th leaf node is given by
bc,m (ot ) = N ot ; μc,m , Σc .
(4)
After training the rich context models in the same manner as in
the conventional method, the output probability density in eachleaf node is modeled by a GMM composed of all rich context
models in the same leaf node [13] as follows :Mcbc (ot ) =ωm N ot ; μc,m , Σc ,(5)

4. Improved Parameter Generation Method
for Spectral and F0 Components
4.1. Better Initialization
To generate a less-smoothed initial parameter sequence, we propose an initialization method with tree-based context clustering.
As shown in Fig. 1, a large-sized tree for context clustering is

m=1

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Likelihood difference

constructed by decreasing parameter a in Eq. (1). Note that thesufficient statistics to build this tree are the same as those usedin calculating rich context models, which is calculated using theconventional clustered models.
In this tree, both the mean vector and the covariance matrixof each probability density function are calculated from only a
few acoustic inventories determined by context factors. Therefore, the initial parameter estimate is less-smoothed than thatgenerated by the conventional clustered model. It can be expected that this initial parameter sequence will help to select aless-smoothed model final sequence after the iterative parameter generation process. On the other hand, the use of a largersized decision tree causes over-fitting problems. In particular,
the initial parameter sequence suffers from many discontinuoustransitions causing the synthetic speech to sound harsh. However, these discontinuous transitions are alleviated by the useof tied covariance matrices and model selection considering the
HMM likelihoods for not only static feature but also dynamic
features during the iterative process described in section 3.2.

bc (ot ) =Mcwc,m N ot ; μc,m , Σc ,static feature vector
delta feature vector
delta-delta feature vector21
Proposed (a = 0.1)

Clus

TargetFigure 2: Differences of HMM likelihood between before andafter iteration.
-60

59
Target58-70
Target-80

56

Log likelihood

Log likelihood

57
55
54 Clus
Proposed

53
52

-90
-100

-120
-130

50
49
0.1

-140
0.3

0.5
a

0.7

0.9

Proposed

-110

51

(a)

Clus
-150
0.1 0.3

0.5 0.7
a

0.9

(b)

Figure 3: Results of objective evaluations of (a) HMM likelihood of selected rich context models for natural parameters, (b)GV likelihood for generated parameters.

lt = V, (10)
lt = U
1 − wc ,where ωc,m is the mixture component weight of the m-th richcontext model in voiced space (ωc,m = ωc /Mc ). We can calculate the ML estimate of ωc,m based on the occupancy counts
but we set it to an constant value based on our previous findings that the constant weight setting is effective in the spectral
component.
In the parameter generation, the initial parameter sequence
is determined by the clustered models with a large-sized decision tree and unvoiced/voiced decision is performed based onthese models. Then, the selection of rich context models andthe generation of a parameter sequence is iteratively performed.
⎩ m=1

3

0

4.2. Implementation for F0 Component
The rich context models of the F0 component are trained in thesame manner as in the continuous HMMs. which is given by
wc N ot ; μc,m , Σc , lt = V
bc,m (ot ) =,(9)
1 − wc ,lt = U
where the weight of the voiced space is also tied over the richcontext models. A GMM in the voiced space is composed ofGaussian distributions in voiced space of all rich context models
in the same leaf node⎧⎨4

ond, we investigate effectiveness of the proposed F0 parameter
generation method with the rich context models. Finally, we investigate the effectiveness of applying the proposed parameter
generation to both spectral and F0 components. Conventional
clustered models were used for duration and aperiodic components in all evaluations.
5.2. Effectiveness of Initialization Method
5.2.1. Confirmation of Alleviating Discontinuous Transition
First, we performed a preliminary experiment to confirm
whether or not the iterative parameter generation effectively alleviates the discontinuous transitions in the initial parametersequence, we evaluated 3 settings of the initial parameter sequences: 1) Clus: generated from the conventional clusteredmodels, 2) Proposed (a = 0.1): generated with a large-sized
decision tree (a = 0.1), and 3) Target: natural target speechparameter sequence as a reference. The difference of HMM
likelihoods for the generated parameters between the initiallyselected rich context model sequence and the finally selected
one was calculated for each static and dynamic features in the
spectral parameters.
The result of the likelihood differences yielded by the iterative parameter generation is shown in Fig. 2. We can seethat the HMM likelihood for dynamic features of “a = 0.1”
increases more than that of the other initial parameter sequence.
From this result, we can see that the discontinuous transitionsin the initial parameter sequence are alleviated by the iterative
parameter generation.

5. Experimental Evaluations
5.1. Experimental Conditions
In the experiments, we trained a context-dependent phoneme
Hidden Semi-Markov Model (HSMM) [20] for a Japanese female speaker. We used 450 sentences for training and 53 sentences for evaluation from the ATR Japanese speech database[21]. Speech signals were sampled at 16 kHz. The 0th-through24th mel-cepstral coefficients were extracted as spectral parameters and log-scaled F0 and 5 band-aperiodicity [22] were extracted as excitation parameters by STRAIGHT [23]. The feature vector consisted of spectral and excitation parameters andtheir delta and delta-delta features. 5-state left-to-right HSMMs
were used. In synthesis, global variance (GV) [24] was not considered.
We conducted 3 kinds of experimental evaluation. First,
we investigate the effectiveness of the proposed initialization
method by applying it to the spectral component and comparing it with the conventional method described in Section 3. Sec-5.2.2. Objective Evaluation
To investigate the effectiveness of the proposed initialization
method, we evaluated 3 settings of initial parameters: 1) Clus,
2) Proposed (a = 0.1, 0.2, · · · , 1.0), and 3) Target. The rich

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1

1

0.8

0.8

0.6

0.6

0.4

0.4

0.2
0

0.2

Clus

a=0.5
a=0.1
Proposed0

Target1
Preference score

0.8
0.6
0.4
0.2
Clus
a=0.6
Proposed

CP

PC

PP

TT

Table 1: Synthetic speech samples used for “Full synthesis”
evaluation. “Target” is generated by parameter generation withrich context models using natural speech parameter sequence as
initial parameter.
methodSpectrum
F0
CC
Conventional
Conventional
CP
Conventional
Proposed (a = 0.6)
PC
Proposed (a = 0.1)
Conventional
PP
Proposed (a = 0.1) Proposed (a = 0.6)
TT
TargetTarget95% confidence interval

Conv

CC

Figure 6: Preference scores on speech quality (Full synthesis).

Figure 4: Preference scores on speech quality (Spectrum).

095% confidence interval

Preference score

Preference score95% confidence interval

TargetFigure 5: Preference scores on speech quality (F0 ).
tree (a = 0.6) for the initial parameter generation in the proposed method, 4) generated using natural target speech parameters as the initial parameters in the proposed method. We set
the parameter a to 0.6 because it was observed that the GV likelihood is highest at this parameter setting in preliminary experiment. A preference test (AB test) on speech quality was conducted by 6 listeners in the same manner as in section 5.2.
The result of the preference test is shown in Fig. 5. It isobserved the score of “Proposed (a = 0.6)” is higher than thatof “Conv.” The proposed parameter generation method is also
effective even for the F0 component.

context model sequences selected by the parameter generation
method using these initial parameter settings were evaluatedwith two criteria that are well-known to measure the improvement in speech quality: the HMM likelihood of the selectedrich context models for the natural speech parameters, and theGV likelihood [24] for the parameter sequence generated by theselected rich context models.
The result of HMM likelihood is shown in Fig. 3(a) and thatof GV likelihood is shown in Fig. 3(b). It is observed from Fig.
3(a) that the HMM likelihood of “Proposed” slightly increases
as the parameter a decreases from 1.0 to 0.5, and it rapidly decreases as the parameter a decreases further. We can see thatthe HMM likelihood at a = 0.5 is almost the same as that of“Clus” but it is significantly lower than that of “Target.” On theother hand, It is observed from Fig. 3(b) that the GV likelihoodof “Proposed” rapidly increases as the parameter a decreases,
and its value at a = 0.1 is higher than that of “Target.”

5.4. Evaluation in Full Synthesis
To investigate the effectiveness of all proposed methods, weevaluated 5 kinds of synthetic speech shown in Table 1. A
preference test (AB test) on speech quality was conducted by
8 listeners in the same manner as in the section 5.2.
The result of the preference test is shown in Fig. 6. It is observed that a larger speech-quality improvement is yielded by
the proposed method for the spectral component than for theF0 component. Moreover, a further improvement is yielded byapplying the proposed method to both spectral and F0 components, and the resulting speech quality shown as “PP” is close to“TT.” From this result, we can see that the proposed parameter
generation with rich context models for spectral and F0 components is very effective to improve quality in synthetic speechand close to the upper bound of initialization using target speechparameters.

5.2.3. Subjective Evaluation
A preference test (AB test) on speech quality was conducted using 4 types of synthetic speech, “Clus,” “Proposed (a = 0.1),”“Proposed (a = 0.5),” and “Target.” Every pair of these 4 typesof synthetic speech was presented to 7 listeners in random order. Listeners were asked which sample sounds better in termsof speech quality.
The result of the preference test is shown in Fig. 4. The
proposed initialization method significantly improves speech
quality compared with the conventional initialization method“Clus.” We can also see that the score of “Proposed (a = 0.1)”
is higher than “Proposed (a = 0.5).” This tendency is the sameas observed in the GV likelihood shown in Fig. 3 (b).

6. Summary
In this paper, we improved a parameter generation method withrich context models by introducing a new parameter initialization method and applied it to both spectral and F0 components in HMM-based speech synthesis. The experimental results demonstrated that the proposed method yields significant
improvements in synthetic speech quality. As future work, wewill study adaptation techniques using rich context models.

5.3. Effectiveness of Proposed F0 Generation
To investigate the effectiveness of the proposed generation
method for F0 , we compared speech quality of speech generated by the proposed method with that generated by conventional parameter generation algorithm with clustered models.
We evaluated 4 kinds of synthetic speech: 1) Conv: generated
from clustered models, 2) Clus: generated using the parametersequence of “Conv” as the initial parameters in the proposed
method, 3) Proposed: generated using the large-sized decision

Acknowledgements: Part of this work was supported by JSPS
KAKENHI Grant Number 24240032, and was executed under the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan.

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