Speech Synthesis as A Statistical
Machine Learning Problem
Keiichi Tokuda
Nagoya Institute of Technology

ASRU2011, Hawaii
December 14, 2011

Introduction
Rule-based, formantfsynthesis (~’90s)( ’90 )– Hand-crafting each phonetic units by rules

Corpus-based, concatenative synthesis (’90s~)– Concatenate speech units (waveform) from a database• Single inventory: diphone synthesis• Multiple inventory: unit selection synthesis

C
Corpus-based,bd statisticalt ti ti l parametrict i synthesisth i– Source-filter model + statistical acoustic model• Hidden
Hidd M
Markovk model:d l HMM-based
HMM bd synthesisth i

How we can formulate and understand the whole
corpus-based speech synthesis process in a
unified statistical framework?

Problem of speech synthesis
We have a speech database, i.e., a set of texts andcorresponding speech waveforms.
fGiven a text to be synthesized, what is the speech
waveform corresponding to the text?

W : texts
X : speech waveforms
w : text to be synthesizedx : speechh waveformf

database

Given

?


Statistical formulation of speech synthesis (1/8)
B
Bayesian
i frameworkfk ffor prediction
di ti: set of texts: speech waveforms

databaseGiven: text to be synthesizedf: speech waveform

unknownk

1. Estimate p
predictive distribution ggiven variables
2. Draw sample from the distribution
3

Statistical formulation of speech synthesis (2/8)
1. Estimating predictive distribution is hard  Introduce acoustic model parameters

generation: acoustic model (e.g. HMM

training)Statistical formulation of speech synthesis (3/8)
2. Using speech waveform directly is difficult  Introduce parametric its representation: parametric representation of speech waveform(e.g., cepstrum, LPC, LSP, F0, aperiodicity)Statistical formulation of speech synthesis (4/8)
3. Same texts can have multiple pronunciations, POS, etc.  Introduce labels: labels derived from text(
(e.g.
prons, POS,
POS lexicall i l stress,t
grammar, pause))Statistical formulation of speech synthesis (5/8)
4. Difficult to perform integral & sum over auxiliary variables  Approximated by joint max

Statistical formulation of speech synthesis (6/8)
5. Joint maximization is hard  Approximated by step-by-step maximizations

Statistical formulation of speech synthesis (7/8)
6. Training also requires parametric form of wav & labels  Introduce them & approx by step-by-step maximizations: parametric representation of speech waveforms: labelsl b l dderivedi d ffrom ttextstStatistical formulation of speech synthesis (8/8)

Overview of this talk
1. Mathematical formulation
2. Implementation of individual components
3. Examples demonstrating its flexibility
4. Discussion and conclusionHMM-based speech synthesis system
SPEECHDATABASE

Text analysis

Speech signal

Excitation
Parameter
extraction

Training part
SpectralParameter
Extraction

Excitation
parameterspSpectralparameterspTraining HMMs
LabelsTEXT
Context-dependent HMMs
& state duration models

T analysis
Text
l iLabelsSynthesis part

Excitation
parameterstParameter generationfrom HMMs

Excitation Excitation
generation

SpectralparameterstSynthesis
FilterSYNTHESIZEDSPEECHHMM-based speech synthesis system
SPEECHDATABASE

Speech signal

Training part
SpectralParameter
Extraction

Excitation
Parameter
extractionLˆ  arg max P( L | W , Λ )L
Text analysis

Excitation
parameterspSpectralparameterspTraining HMMs

Labelsλˆ  arg max P(O | Lˆ , λ)λTEXToˆ  arg max P(o | lˆ, λˆ)Context-dependent HMMso& state duration models

T analysis
Text
l iParameter generationfrom HMMs

ˆl  arggLabels
Excitation
max Pparameters(l | wt , Λ )lSynthesis part

Excitation Excitation
generation

SpectralparameterstSynthesis
FilterSYNTHESIZEDSPEECHHMM-based speech synthesis system
SPEECHDATABASE

Text analysis

Speech signal

Excitation
Parameter
extraction

Training part
SpectralParameter
Extraction

Excitation
parameterspSpectralparameterspTraining HMMs
LabelsTEXT
Context-dependent HMMs
& state duration models

T analysis
Text
l iLabelsSynthesis part

Excitation
parameterstParameter generationfrom HMMs

Excitation Excitation
generation

SpectralparameterstSynthesis
FilterSYNTHESIZEDSPEECHHuman speech production

Modulation of carrier wave
by speech information
Frequencytransfercharacteristics

SpeechSound source
Voiced: pulse
Unvoiced: noise

Magnitude
start--end
Fundamental
frequency
air flowSource-filter model

Source excitation part
Pulse train

Excitatione(n)White noise

Vocal tract resonance part
Lineartime-invariant
systemH ((z )

Speechx ( n )  h ( n ) * e( n )ML estimation of spectral parameter

mm~
H ( z )  exp
exp
c
(
m
)
z
c
m
z
(
)


m00
m

z 
1
 j~
~
z 
e
1
1 z
1

Warrped freque
ency

M
M

(rad
d)

Mel-cepstral representation of speech spectra
2

warped frequency
mel-scale frequency

0

 /2

Frequency  (rad)

ML ti ti off mel-cepstrum
ML-estimationlt
x : speech waveform (Gaussian process)c  arg max p ( x | c ) c: mel-cepstrumcWaveform reconstruction
Original speech

F0

Unvoiced / voiced

Mel-cepstrum

P l train
t i
Pulse

Synthesis filter
Excitation
White noisee(n)H (z )

reconstructed speechx(n)These speech parameters should be modeled by HMMHMM-based speech synthesis system
SPEECHDATABASE

Text analysis

Speech signal

Training part

Excitation
Parameter
extraction

SpectralParameter
Extraction

Excitation
parameterspSpectralparameterspTraining HMMs

Labelsλˆ  arg max P(O | Lˆ , λ)λTEXTContext-dependent HMMs
& state duration models

T analysis
Text
l iLabelsSynthesis part

Excitation
parameterstParameter generationfrom HMMs

Excitation Excitation
generation

SpectralparameterstSynthesis
FilterSYNTHESIZEDSPEECHHidden Markov model (HMM)

aij: state transition probabilitya11bq (ot ) : output probability1b1 (ot )

a33

a22
a12

2

a23

b2 (ot )3b3 (ot )Observation
sequenceqoo1 o2 o3 o4 o5  ・ ・ Statesequenceq

1 1 1 1 2  2 3

oT

 3


Structure of state output (observation) vector
Spectral parameters((e.g.,
g mel-cepstrum,pLSPs))
Spectrum part
log F0 with V/UV
Excitation part





Observation of F0
unvoicedvoiced2  R01  R1y
Log Frequency

・

Time

Unable to model by continuous or discrete distributions⇒ Multi-space distribution HMM (MSD-HMM)
Multi-space probablity distribution HMM(MSD-HMM)
11  R

w2,1

w1,1

n1

N11 ( x )

2  RM  RnM

w3,1

N 21 ( x )
w2, 2

w1, 2

n2

3

2

N 31 ( x )w3, 2

N12 ( x )N 22 ( x )N 32 ( x )







w1, M
N1 M ( x )w2, M

N 2M ( x)w3, M
N 3M ( x )MSD-HMM for F0 modeling1

HMM for F01  R1

w2,1

w1,1
Voiced2  R

0

w1, 2

・

Unvoiced

3

2
w3,1

Voicedw2, 2

・

Unvoiced

Voicedw3, 2・

Unvoiced

Voiced / Unvoiced weights
Structure of state-output distributions
οt

S
Spectrummct

Mel-cepstrum

c t

VoicedUnvoiced

 ct・

Exccitation

2

pt
 pt
2
 pt

VoicedLog F0Unvoiced・

VoicedUnvoiced・Contextual factors
Phoneme{preceding, succeeding} two phonemes
current phoneme

Syllable












# of phonemes in {preceding, current, succeeding} syllable{accent, stress} of {preceding, current, succeeding} syllable
Position of current syllable in current word
# off {{preceding,disucceeding}di } {{accented,
t d stressed}td} syllable
ll bl iin currentt phraseh# of syllables {from previous, to next} {accented, stressed} syllable
Vowel within current syllable

Word










Part of speech of {preceding, current, succeeding} word
# of syllables in {preceding, current, succeeding} word
Position of current word in current phrase# of {preceding, succeeding} content words in current phrase# of words {from previous, to next} content word

Phrase# of syllables in {preceding, current, succeeding} phrase…..

Huge # of combinations ⇒ Difficult to have all possible models
Decision tree-based state clustering [Odell; ’95]
L=voice?

k-a+b

yes

no

L=“w”?

t-a+h

yes

R=silence?
yes

no

no

R=silence?

…

…

s-i+n

C=unvoice?
C=vowel?
R=silence?
R=“cl”?
L=“gy”?
L=voice?

yesyL=“gy”?
no

yes
y

no

…

…

Sharing the parameter of HMMs in same leaf nodeStream-dependent tree-based clustering (1)Spectrum & excitation have differentSffcontext
dependency  Build decision trees separately

Decision treesformel-cepstrum
Decision trees
for F0State duration modeling
HMM (Hidden Markov Model)– State duration prob. depends only on transition prob.
– State duration probability exponentially decreases

HSMM (Hidden Semi Markov Model)– HMM + explicit duration model ⇒ HSMM
3-demensional
3
demensional Gaussian

q1
d1

q3

q2
d2

K

d3

P ( q | lˆ , λˆ )   p i d i i 1

O1 O2 O3 O4 O5 O6 O7 O8 O9Stream-dependent tree-based clustering (2)State duration
model

Three dimensional Gaussian

HMM
DecisionD
i i ttreesformel-cepstrum

Decision tree
for state dur.
models

Decision trees
for F0HMM-based speech synthesis system
SPEECHDATABASE

Text analysis

Speech signal

Training part
SpectralParameter
Extraction

Excitation
Parameter
extraction
Excitation
parameterspSpectralparameterspTraining HMMs
LabelsTEXToˆ  arg max P(o | lˆ, λˆ)Context-dependent HMMs
& state duration models

T analysis
Text
l i

o

LabelsParameter generationfrom HMMs

Synthesis part

Excitation
parameterst

Excitation Excitation
generation

SpectralparameterstSynthesis
FilterSYNTHESIZEDSPEECHComposition of sentence HMM for given text
TEXT

w

G2P
POS tagging

Text analysis

Text normalization
Pause prediction

context-dependent label
sequence lˆsentence HMM given labels

This sentence HMM givesp (o | lˆ, λˆ)Speech parameter generation algorithm [Tokuda; ’00]
For given sentence HMM, determine a speech parameter
T
T
T T
o

[
o
,
o
,

,
o
vector sequence
1
2
T ] which maximizes
P (o | lˆ, λˆ)   P(o | q, λˆ)P (q | lˆ, λˆ)q max P(o | q, λˆ) P(q | lˆ, λˆ)q⇒qˆ  arg max P (q | lˆ, λˆ)qoˆ  arg max P (o | qˆ , λˆ)oDetermination of state sequenceKP ( q | lˆ , λˆ )   p i d i i 1

p i   : state-duration distribution of i -th stated i : state duration of i -th state

K : # of states in a sentence HMM for lˆGaussianpi d i   N d i | mi , 2i  dˆ  miiSpeech parameter generation algorithm
For given HMM λ ,determine a speech parameter vector
T
T
T T
o

[
o
,
o
,

,
o
Sequence
1
2
T ] which maximizes
P (o | lˆ, λˆ)   P(o | q, λˆ)P (q | lˆ, λˆ)q max P(o | q, λˆ) P(q | lˆ, λˆ)q⇒qˆ  arg max P (q | lˆ, λˆ)qoˆ  arg max P (o | qˆ , λˆ)oWithout dynamic feature

becomes a sequence of mean vectors⇒ discontinuous outputs between statesDynamic features

ct
ct 
 0.5(ct 1  ct 1 )
t
2
 ct
2
 ct  2  ct 1  2ct  ct 1
t
ct 1

ct

ct 1

ct 1

ct

ct 1

 0.5

0.5

1

2

1

cct 1cct 1cct 1


2 ct

ct

cct 1Integration of dynamic features
Relationship between speech parameter vectors &static feature vectors
o
c
W
T



ot  ctT , ctT , 2 ctT
M

M



M

3M



Solution for the problem (1/2)
O
By setting

 log P (Wc | qˆ ,  ) O,cwe obtain

W T Σ qˆ 1Wc  W T Σ qˆ 1 μqˆ ,wherehc  [c1T , c 2T ,  , cTT ]Tμqˆ  [ μ , μ ,  , μ ]
T
qˆ1

T
qˆ 2

T T
qˆT

Σ qˆ  [ Σ , Σ ,  , Σ ]
T
qˆ1

T
qˆ 2

T T
qˆT


Solution for the problem (2/2)
1
qˆ

W

T

Σ

W

T

Σ qˆ 1

W

c

μqˆ




Generated speech parameter trajectoryTrajectory HMM
P (o | l , λ)  P (Wc | l ,  ) is not a proper distribution of cConventional HMM
Training

Trajectory HMM

arg max P(O | Lˆ , λ) arg max P (C | Lˆ , λ)λλarg max P (o | lˆ, λˆ ) |o Wc
Synthesiso arg
g max P (c | lˆ, λˆ )cˆ, λˆ)
arg
g max
P
(
c
|
l
ˆ
λ̂

Solve inconsistencyy between trainingg & synthesisy⇒ improving the model accuracyGenerated spectra
sil

a

i

sil

0 1 2 3 4 5(kHz)

w/o dyn0 1 2 3 4 5(kkHz)w dyn

Spectra changing smoothly between phonemesGenerated F0
Freqquencynatural speech
200
150
100
00
50
0

1

2

time (s)Freequency

without dynamic features
200
150
100
50
0

1

2

time (s)F
Frequencywith dynamic features
200
150
100
50
0

1

2

time (s)Effect of dynamic features
Mel-cepstrum
static+

2staticstatic+2log F0

Smooth!
SoostaticOverview of this talk
1. Mathematical formulation
2. Implementation of individual components
3. Examples demonstrating its flexibility
4. Discussion and conclusion

Emotional speech synthesis
textneutralangry「授業中に携帯いじってんじゃねえよ！
電源切っとけ！」“Don’tDon’t touch your cell phone during aclass! Turn off it!”
「ミーティングには毎週参加しなさい！」「ミ
ティングには毎週参加しなさい！」“You must attend the weekly meeting!”

trained with 200 utterances

Speaker adaptation (mimicking voices)
MLLR-based adaptation
Adaptationdata
speaker A

Speaker-independent

adaptation

Adapted model

w/o adaptation (initial model)
Adapted with 4 utterances
Adapted with 50 utterances
S
Speakerk A’
A’s speaker-dependent
k d
d t systemt？

Speaker interpolation (mixing voices)
Linear combination of two speaker-dependent models

Model A

Model B
Interpolated model

A: 1.00

0.75

0.50

0.25

0.00

B: 0.00

0.25

0.50

0.75

1.00

Voice morphing
Two voices:  

Four voices:Interpolation of speaking styles

Base model A

Base model B

Interpolation

Neutralextrapolation

High Tension

Eigenvoice (producing voices) [Shichiri; ’02]
Speaker 1

Speaker 2

μ

e (1)

Speaker S

e (k )e( K )
System Overview
Click here
for a demoMultilingual speech synthesis
•
•
•
•
•
•
•
•
•
•
•
•

JJapanese
American English
Chinese ((Mandarin)) ((byy ATR))
Brazilian Portuguese (by Nitech, and ＵＦＲＪ)
European Portuguese (by Nitech, Univ of Porto, and UFRJ)
Slovenian(by Bostjan Vesnicer, University of Ljubljana, Slovenia)
Swedish (by Anders Lundgren, KTH, Sweden)
German (by University of Bonn
Bonn, and Nitech)
Korean (by Sang-Jin Kim, ETRI, Korea)
Finish (by TKK, Finland)
Baby English (by Univ of Edinburgh, UK)
Polish, Slovak, Arabic, Farsi, Croatian, Polyglot, etc.

Singing voice synthesis [Oura; ’10] (1/2)
MusicXML

Trained HMMs

Male:
Female:Singing voice
database

Musical score

Synthesized
singing voice
Male:Female:
Ad t ti
Adaptation:
Overview of this talk
1. Mathematical formulation
2. Implementation of individual components
3. Examples demonstrating its flexibility
4. Discussion and conclusionInclusion of all components
Problem of statistical parametric speech synthesisˆ from P x | w , X , W
Draw x̂   P ( x | c ) Pc | l , λ  Pl | w , Λ l, LWaveform generation Parameter generation Text processing P λ | C , LP t i off model
Posteriord l parametert P L |W , Λ  PC|X  PΛ  dλ dΛ dc dC

Text processing

Speech analysis

Prior


Relaxing approximations
Marginalizing model parameters
Variational Bayesian acoustic modeling for speech synthesis[N k k ’03]
[Nankaku;’03]

Marginalizing labels
J i t front-end
Jointf t d / back-end
b k d modeld l ttraining
i i [O[Oura;’08]’08]

Inclusion of waveform generation partW
Waveform-levelfll statisticalt ti ti l modeld l [M[Maia;’10]
i ’10]

Summary
Statistical approach to speech synthesis• Whole speech synthesis process is described in astatistical framework• It gives a unified view and reveals what is correct
and what is wrongg• Importance of the database
Future work
• Still we have many problems should be solved:• Speech waveform modeling• Combination with text processing part, etc.


Final message

I speechIsh synthesisth i a messy problem?
bl ?

Let us join speech synthesis research!

Thanks!
References (1/4)
Sagisaka;'92
S
i k '92 - "ATR nu-TALK
TALK speechh synthesisth i system,"t" ICSLPICSLP, '92'92.
Black;'96 - "Automatically clustering similar units...," Euro speech, '97.
Beutnagel;'99 - "The AT&T Next-Gen TTS system," Joint ASA, EAA, & DAEA meeting, '99.
Yoshimura;'99 - "Simultaneous modeling of spectrum ...," Eurospeech, '99.
Itakura;'70
Itakura;70 - "AA statistical method for estimation of speech spectral density
density...," Trans.
Trans IEICE,IEICE J53-A,
J53-A '70
70.
Imai;'88 - "Unbiased estimator of log spectrum and its application to speech signal...," EURASIP, '88.
Kobayashi;'84 - "Spectral analysis using generalized cepstrum," IEEE Trans. ASSP, 32, '84.
Tokuda;'94 - "Mel-generalized cepstral analysis -- A unified approach to speech spectral...," ICSLP, '94.
Imai;'83;- "Cepstralpanalysisysynthesisyon the mel frequencyqy scale,", ICASSP,, '83.
Fukada;'92 - "An adaptive algorithm for mel-cepstral analysis of speech," ICASSP, '92.
Itakura;'75 - "Line spectrum representation of linear predictive coefficients of speech...," J. ASA (57), '75.
Tokuda;'02 - "Multi-space probability distribution HMM," IEICE Trans. E85-D(3), '02.
Odell;'95 - "The use of context in large vocaburary...," PhD thesis, University of Cambridge, '95.
Shinoda;'00 - "MDL-based context-dependent subword modeling...," Journal of ASJ(E) 21(2), '00.
Yoshimura;'98 - "Duration modeling for HMM-based speech synthesis," ICSLP, '98.
Tokuda;'00 - "Speech parameter generation algorithms for HMM-based speech synthesis," ICASSP, '00.
Kobayashi;'85 - "Mel generalized-log spectrum approximation...," IEICE Trans. J68-A (6), '85.
Hunt;'96
Hunt;
96 - "Unit
Unit selection in a concatenative speech synthesis system using...,using " ICASSP,ICASSP '96
96.
Donovan;'95 - "Improvements in an HMM-based speech synthesiser," Eurospeech, '95.
Kawai;'04 - "XIMERA: A new TTS from ATR based on corpus-based technologies," ISCA SSW5, '04.
Hirai;'04 - "Using 5 ms segments in concatenative speech synthesis," Proc. ISCA SSW5, '04.

References (2/4)
Rouibia;'05
R
ibi '05 - "Unit"U it selection
l ti ffor speechh synthesisth i bbased
d on a new acoustic
ti ttargett cost,"t " IInterspeech,
t
h '05
'05.
Huang;'96 - "Whistler: A trainable text-to-speech system," ICSLP, '96.
Mizutani;'02 - "Concatenative speech synthesis based on HMM," ASJ autumn meeting, '02.
Ling;'07 - "The USTC and iFlytek speech synthesis systems...," Blizzard Challenge workshop, 07.
Ling;'08
Ling;
08 - "Minimum
Minimum unit selection error training for HMM-based unit selection
selection...," ICASSP,ICASSP 08
08.
Plumpe;'98 - "HMM-based smoothing for concatenative speech synthesis," ICSLP, '98.
Wouters;'00 - "Unit fusion for concatenative speech synthesis," ICSLP, '00.
Okubo;'06 - "Hybrid voice conversion of unit selection and generation...," IEICE Trans. E89-D(11), '06.
Aylett;'08
y ;- "Combining
g statistical pparametric speechpsynthesisyand unit selection..." LangTech,g, '08.
Pollet;'08 - "Synthesis by generation and concatenation of multiform segments," Interspeech, '08.
Yamagishi;'06 - "Average-voice-based speech synthesis," PhD thesis, Tokyo Inst. of Tech., '06.
Yoshimura;'97 - "Speaker interpolation in HMM-based speech synthesis system," Eurospeech, '97.
Tachibana;'05 - "Speech synthesis with various emotional expressions...," IEICE Trans. E88-D(11), '05.
Kuhn;'00 - "Rapid speaker adaptation in eigenvoice space," IEEE Trans. SAP 8(6), '00.
Shichiri;'02 - "Eigenvoices for HMM-based speech synthesis," ICSLP, '02.
Fujinaga;'01 - "Multiple-regression hidden Markov model," ICASSP, '01.
Nose;'07 - "A style control technique for HMM-based expressive speech...," IEICE Trans. E90-D(9), '07.
Yoshimura;'01
Yoshimura;
01 - "Mixed
Mixed excitation for HMM
HMM-basedbased speech synthesis
synthesis," Eurospeech,
Eurospeech '01
01.
Kawahara;'97 - "Restructuring speech representations using a ...", Speech Communication, 27(3), '97.
Zen;'07 - "Details of the Nitech HMM-based speech synthesis system...", IEICE Trans. E90-D(1), '07.
Abdl-Hamid;'06 - "Improving Arabic HMM-based speech synthesis quality," Interspeech, '06.

References (3/4)
Hemptinne;'06Hti'06 - "Integration"I tti off th
the harmonichi plusl noisei modeld l iintot ththe...,"" MasterM t thesis,th i IDIAP,IDIAP '06
'06.
Banos;'08 - "Flexible harmonic/stochastic modeling...," V. Jornadas en Tecnologias del Habla, '08.
Cabral;'07 - "Towards an improved modeling of the glottal source in...," ISCA SSW6, '07.
Maia;'07 - "An excitation model for HMM-based speech synthesis based on ...," ISCA SSW6, '07.
Ratio;'08
Ratio;
08 - "HMM-based
HMM-based Finnish text-to-speech system utilizing glottal inverse filteringfiltering," Interspeech,
Interspeech '08
08.
Drugman;'09 - "Using a pitch-synchronous residual codebook for hybrid HMM/frame...", ICASSP, '09.
Dines;'01 - "Trainable speech synthesis with trended hidden Markov models," ICASSP, '01.
Sun;'09 - "Polynomial segment model based statistical parametric speech synthesis...," ICASSP, '09.
Bulyko;'02
y ;- "Robust splicing
pg costs and efficient search with BMM models for...,", ICASSP,, '02.
Shannon;'09 - "Autoregressive HMMs for speech synthesis," Interspeech, '09.
Zen;'06 - "Reformulating the HMM as a trajectory model...", Computer Speech & Language, 21(1), '06.
Wu;'06 - "Minimum generation error training for HMM-based speech synthesis," ICASSP, '06.
Hashimoto;'09 - "A Bayesian approach to HMM-based speech synthesis," ICASSP, '09.
Wu;'08 - "Minimum generation error training with log spectral distortion for...," Interspeech, '08.
Toda;'08 - "Statistical approach to vocal tract transfer function estimation based on...," ICASSP, '08.
Oura;'08 - "Simultaneous acoustic, prosodic, and phrasing model training for TTS...," ISCSLP, '08.
Ferguson;'80 - "Variable duration models...," Symposium on the application of HMM to text speech, '80.
Levinson;'86
Levinson;86 - "Continuously
Continuously variable duration hidden
hidden...," Computer Speech & Language
Language, 1(1)1(1), '86
86.
Beal;'03 - "Variational algorithms for approximate Bayesian inference," PhD thesis, Univ. of London, '03.
Masuko;'03 - "A study on conditional parameter generation from HMM...," Autumn meeting of ASJ, '03.
Yu;'07 - "A novel HMM-based TTS system using both continuous HMMs and discrete...," ICASSP, '07.

References (4/4)
Qian;'08Qi'08 - "Generating"Gti natural
t l F0 ttrajectoryj t
withith additive
dditi ttrees,"" IInterspeech,
t
h '08
'08.
Latorre;'08 - "Multilevel parametric-base F0 model for speech synthesis," Interspeech, '08.
Tiomkin;'08 - "Statistical text-to-speech synthesis with improved dynamics," Interspeech, '08.
Toda;'07 - "A speech parameter generation algorithm considering global...," IEICE Trans. E90-D(5), '07.
Wu;'08Wu;
08 - "Minimum
Minimum generation error criterion considering global/local variancevariance...," ICASSP,ICASSP '08
08.
Toda;'09 - "Trajectory training considering global variance for HMM-based speech...," ICASSP, '09.
Saino;'06 - "An HMM-based singing voice synthesis system," Interspeech, '06.
Tsuzuki;'04 - "Constructing emotional speech synthesizers with limited speech...," Interspeech, '04.
Sako;'00;- "HMM-based text-to-audio-visual speechpsynthesis,"y, ICSLP,, '00.
Tamura;'98 - "Text-to-audio-visual speech synthesis based on parameter generation...," ICASSP, '98.
Haoka;'02 - "HMM-based synthesis of hand-gesture animation," IEICE Technical report,102(517), '02.
Niwase;'05 - "Human walking motion synthesis with desired pace and...," IEICE Trans. E88-D(11), '05.
Hofer;'07 - "Speech driven head motion synthesis based on a trajectory model," SIGGRAPH, '07.
Ma;'07 - "A MSD-HMM approach to pen trajectory modeling for online handwriting...," ICDAR, '07.
Morioka;'04 - "Miniaturization of HMM-based speech synthesis," Autumn meeting of ASJ, '04.
Kim;'06 - "HMM-Based Korean speech synthesis system for...," IEEE Trans. Consumer Elec., 52(4), '06.
Klatt;'82 - "The Klatt-Talk text-to-speech system," ICASSP, '82.

Acknowledgement

Keiichi Tokuda would like to thank HTS working
groupgp members, includingg Heigag Zen, Keiichiro
Oura, Junichi Yamagichi, Tomoki Toda, YoshihikoNankaku, Kei Hahimoto, and Sayaka Shiota for
their help.

HTS Slides
released by HTS Working Group
http://hts.sp.nitech.ac.jp/ppjp

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Nagoya Institute of Technology
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