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IEICE Transactions on Information and Systems 2007 E90-D(5):825-834; doi:10.1093/ietisy/e90-d.5.825
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Copyright © 2007 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Speech and Hearing

A Hidden Semi-Markov Model-Based Speech Synthesis System

Heiga ZEN1, Keiichi TOKUDA1, Takashi MASUKO2,3, Takao KOBAYASIH2 and Tadashi KITAMURA1

1 The authors are with the Department of Computer Science and Engineering, Nagoya Institute of Technology, Nagoya-shi, 466–8555 Japan. E-mail: zen{at}lavender.ics.nitech.ac.jp, E-mail: tokuda{at}nitech.ac.jp and E-mail: kitamura{at}nitech.ac.jp, 2 The authors are with the Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Yokohama-shi, 226–8502 Japan. E-mail: takao.kobayashi{at}ip.titech.ac.jp, 3 Presently, with the Corporate Research & Development Center, Toshiba Corporation.


  Abstract

A statistical speech synthesis system based on the hidden Markov model (HMM) was recently proposed. In this system, spectrum, excitation, and duration of speech are modeled simultaneously by context-dependent HMMs, and speech parameter vector sequences are generated from the HMMs themselves. This system defines a speech synthesis problem in a generative model framework and solves it based on the maximum likelihood (ML) criterion. However, there is an inconsistency: although state duration probability density functions (PDFs) are explicitly used in the synthesis part of the system, they have not been incorporated into its training part. This inconsistency can make the synthesized speech sound less natural. In this paper, we propose a statistical speech synthesis system based on a hidden semi-Markov model (HSMM), which can be viewed as an HMM with explicit state duration PDFs. The use of HSMMs can solve the above inconsistency because we can incorporate the state duration PDFs explicitly into both the synthesis and the training parts of the system. Subjective listening test results show that use of HSMMs improves the reported naturalness of synthesized speech.

Key Words: hidden Markov model, hidden semi-Markov model, HMM-based speech synthesis

Manuscript received July 27, 2006. Manuscript revised December 11, 2006.


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