Pitch pattern generation using multispace probability distribution HMM

Hironori Nakatani, Takashi Watanabe, Shigeo Ohba, Nozomu Hoshimiya

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


A scheme for simultaneously modeling and generating a pitch pattern and a spectral sequence on the basis of a hidden Markov model (HMM) is presented. Since a pitch pattern is expressed as a time series of voiced intervals taking continuous values and voiceless intervals without values, it cannot be modeled by the usual HMM. This paper proposes a scheme for modeling a pitch and a spectrum integrally with characteristic parameters that combine pitch parameters and spectral parameters by applying an HMM based on a multispace probability distribution (multispace probability distribution HMM: MSD-HMM). In addition, a context clustering scheme based on decision trees in the MSD-HMM is derived, and a scheme for constructing the model while taking account of the variation factors of the pitch and the spectrum is presented. In addition, it is shown that pitch patterns and spectral sequences approximating real voice can be generated by using the parameter generation scheme based on the maximum likelihood criterion.

Original languageEnglish
Pages (from-to)62-72
Number of pages11
JournalSystems and Computers in Japan
Issue number6
Publication statusPublished - 2002 Jun 15


  • Context clustering
  • Hidden Markov model
  • Multispace probability distribution
  • Pitch pattern generation
  • Voice synthesis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture
  • Computational Theory and Mathematics


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