Presentation 2008-12-10
Bayesian Context Clustering Using Cross Validation for HMM-Based Speech Synthesis
Kei HASHIMOTO, Heiga ZEN, Yoshihiko NANKAKU, Keiichi TOKUDA,
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Abstract(in English) This paper proposes a prior distribution determination technique using cross validation for HMM-based speech synthesis based on the Bayesian approach. The Bayesian method is a statistical technique for estimating reliable predictive distributions by marginalizing model parameters and its approximate version, the variational Bayesian method has been applied to HMM-based speech synthesis. Since prior distributions representing prior information about model parameters affect the model selection (e.g., decison tree based context clustering), the determination of prior distributions is an important problem. The proposed method can determine reliable prior distributions without tuning parameters and select an appropriate model structure dependently on the amount of training data.
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Keyword(in English) Bayesian criterion / HMM-based speech synthesis / context clustering / prior distribution / cross validation
Paper # NLC2008-36,SP2008-91
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Committee NLC
Conference Date 2008/12/2(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Bayesian Context Clustering Using Cross Validation for HMM-Based Speech Synthesis
Sub Title (in English)
Keyword(1) Bayesian criterion
Keyword(2) HMM-based speech synthesis
Keyword(3) context clustering
Keyword(4) prior distribution
Keyword(5) cross validation
1st Author's Name Kei HASHIMOTO
1st Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology()
2nd Author's Name Heiga ZEN
2nd Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology
3rd Author's Name Yoshihiko NANKAKU
3rd Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology
4th Author's Name Keiichi TOKUDA
4th Author's Affiliation Department of Computer Science and Engineering, Nagoya Institute of Technology
Date 2008-12-10
Paper # NLC2008-36,SP2008-91
Volume (vol) vol.108
Number (no) 337
Page pp.pp.-
#Pages 6
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