HMM-based speech synthesis: the new generation of artificial voices

15/04/2315/04/23

HMM-based speech synthesis: the new

generation of artificial voices

Thomas [email protected]

22Drugman ThomasDrugman Thomas

TCTS Lab

« Laboratoire de Théorie des Circuits et de Traitement du Signal »

25 people : 3 Profs, 10 PhD Students

Audio& Speech

Image& Video

NumericalArts

TCTS Lab

33

Content

• Speech synthesis: history

• HMM-based speech synthesis

• Parametric modeling of speech

• Statistical generation

• Conclusions

44

Content





• Conclusions


Speech Synthesis

« Hello »Text-to-speech

system

GOAL :

Produce the lecture of an unknown text typed by the user


Challenges

Naturalness

Intelligibility

Cost-effectiveness

Expressivity

77

Challenge 3 : Cost-effectivenessChallenge 3 : Cost-effectiveness

Industry expects Intelligibility + Naturalness + …

Small footprint : a few Megs Small CPU requirements (embedded market) Easy extension to other languages Possibility to create new voices as fast as

possible• Through automatic recording/segmentation

process• Through efficient voice conversion

Possibility to bootstrap an existing TTS voice into any voice

Drugman ThomasDrugman Thomas

88

Challenge 4 (new) : ExpressivityChallenge 4 (new) : Expressivity

=“Emotional speech synthesis” (art!)

1. Being able to render an expressive voice• In terms of prosody• In terms of voice quality

2. Knowing when to do it (yet unsolved)

Today’s holy grail for the industry• Strategic advantage for whoever gets it first• News markets (ebooks?)

Drugman ThomasDrugman Thomas


Methods for Speech Synthesis

Expert-based (rule-based) approach

Corpus-based approach

• Diphone concatenation

• Unit Selection

• Statistical parametric synthesis (“HMM-based synthesis”)

1010Prof. Thierry DutoitProf. Thierry Dutoit

Von Kempelen’s talking machine (1791)

Mouth

Nostrils

Main bellows

Small bellows

'S' pipe

'Sh' pipe

'Sh' lever'S' lever


Omer Dudley’s Voder (Bell Labs, 1936)

NoiseSource

Oscillator

Resonnance Control Amplifier

106 7 8

9

"Quiet"

t-dp-b

k-g

Energy switchwrist bar

VoderConsoleKeyboard

12 3 4

5

Pitch-controlpedal

UV

V

And other developments in articulatory synthesis

Work by :K. Stevens, G. Fant, P. Mermelstein, R. Carré (GNUSpeech), S. Maeda, J. Shroeter & M. Sondhi…

More recently : O. Engwall, S. Fels (ArtiSynth), Birkholz and Kröger, A. Alwan & S. Narayanan (MRI)…



Rule-based synthesis

Intelligibility Naturalness Mem/CPU/Voices Expressivity


Methods for Speech Synthesis

Expert-based (rule-based) approach

Corpus-based approach

• Diphone concatenation

• Unit Selection

• Statistical parametric synthesis (“HMM-based synthesis”)

1515

Diphone concatenation

Intelligibility Naturalness~ Mem/CPU/Voices Expressivity

1616

Unit selection

Intelligibility Naturalness Mem/CPU/Voices ~ Expressivity ~

1717

Content





• Conclusions

1818

Statistical Parametric Speech Synthesis

DATABASESpeech

Parameters

SpeechParameters

SPSSynthesizer

SpeechProcessing

SpeechAnalysis

StatisticalModeling

StatisticalGeneration

TRAININGSYNTHESIS

Hello!« Hello !»

1919

HMM-based speech synthesis

Intelligibility Naturalness ? Mem/CPU/Voices Expressivity ?

http://hts.sp.nitech.ac.jp/

2020

TRAININGOF THE HMM-BASED

SYNTHESIZER

2121

Parameter extraction

2222

Parameter extraction

Pulsetrain

White noise

Filter SyntheticSpeech

2323

Labels

2424

Labels

Labels consist of phonetic environment description

Contextual factors:

-Phone identity-Syntaxical factors-Stress-related factors-Locational , …

2525

Labels

Example

2626

HMM training

2727

System architecture

Contextual factors may affect duration, source and filter

differently

Context Oriented Clusteringusing Decision Trees

2828

State DurationModel

HMM forSource and Filter

Decision treesfor Filter

Decision treesfor Source

Decision treefor

State Duration

System architecture

2929

Training decision trees

An exhaustive list of possible questions is first drawn up

QS "LL-Nasal" {m^*,n^*,en^*,ng^*}QS "LL-Fricative" {ch^*,dh^*,f^*,hh^*,hv^*,s^*,sh^*,th^*,v^*,z^*,zh^*}QS "LL-Liquid" {el^*,hh^*,l^*,r^*,w^*,y^*}QS "LL-Front" {ae^*,b^*,eh^*,em^*,f^*,ih^*,ix^*,iy^*,m^*,p^*,v^*,w^*}QS "LL-Central" {ah^*,ao^*,axr^*,d^*,dh^*,dx^*,el^*,en^*,er^*,l^*,n^*,r^*,s^*,t^*,th^*,z^*,zh^*}QS "LL-Back" {aa^*,ax^*,ch^*,g^*,hh^*,jh^*,k^*,ng^*,ow^*,sh^*,uh^*,uw^*,y^*}QS "LL-Front_Vowel" {ae^*,eh^*,ey^*,ih^*,iy^*}QS "LL-Central_Vowel" {aa^*,ah^*,ao^*,axr^*,er^*}QS "LL-Back_Vowel" {ax^*,ow^*,uh^*,uw^*}QS "LL-Long_Vowel" {ao^*,aw^*,el^*,em^*,en^*,en^*,iy^*,ow^*,uw^*}QS "LL-Short_Vowel" {aa^*,ah^*,ax^*,ay^*,eh^*,ey^*,ih^*,ix^*,oy^*,uh^*}QS "LL-Dipthong_Vowel" {aw^*,axr^*,ay^*,el^*,em^*,en^*,er^*,ey^*,oy^*}QS "LL-Front_Start_Vowel" {aw^*,axr^*,er^*,ey^*}

Example :

Total: about 1500 questions

3030

Training decision trees

Decision trees are trained using a Maximum Likelihood criterion

Example :

3131

Emission likelihood and training

Finally, each leaf is modeled by a Gaussian Mixture Model (GMM)

Training is guided by the Viterbi and Baum-Welch re-estimation

algorithms

3232

SYNTHESISBY THE HMM-BASED

SYNTHESIZER

3333

Text analysis

3434

Parameters generation

3535


Given the sequence of labels, durations are determined by

maximizing the state sequence likelihood

A trajectory through context-dependent HMM states is known !

3636


Using this trajectory, source and filter parameters are generated by maximizing the output probability

Dynamic features evolution more realistic and smooth

3737

Speech synthesizers comparison

3838


UnitSelection

DiphoneConcatenation

HTS

<1Mb 5Mb 200Mb

Quality

Footprint

3939

Content





• Conclusions

4040

Problem positioning

Parametric speech synthesizersgenerally suffer from a typical

buzziness as encountered in LPC-like vocoders

Source–Filter approach:

Enhance the excitation signal

Pulsetrain

White noise

Filter SyntheticSpeech

4141

Proposed solutionSOURCE

FILTER

T.Drugman, G.Wilfart, T.Dutoit, « A Deterministic plus Stochastic Model of the Residual Signal for Improved Parametric Speech Synthesis », Interspeech09

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Results

Traditional:

Proposed:

4343

Content





• Conclusions


Problem of oversmoothing


Compensation of oversmooting


Global Variance


Results

4949

Content





• Conclusions

5050


Intelligibility Naturalness ? Mem/CPU/Voices Expressivity ?

Intelligibility Naturalness Mem/CPU/Voices Expressivity

Intelligibility Naturalness~ Mem/CPU/Voices Expressivity

Intelligibility Naturalness Mem/CPU/Voices ~ Expressivity ~

Rule-based synthesis

Diphone concatenation

Unit selection

HMM-based speech synthesis

5151


UnitSelection

DiphoneConcatenation

HTS

<1Mb 5Mb 200Mb

Quality

Footprint

5252

Future Works

Voice Conversion

Expressive/emotional synthesis

Better parametric representation

Real-time speech synthesis

5353

Questions ?

HMM-based speech synthesis: the new generation of artificial voices

Education

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