Speech Synthesis Based on Hidden Markov Models

K. Tokuda, Y. Nankaku, T. Toda, H. Zen, J. Yamagishi, K. Oura

Research output: Contribution to journalArticlepeer-review


This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.
Original languageEnglish
Pages (from-to)1234-1252
Number of pages19
JournalProceedings of the IEEE
Issue number5
Publication statusPublished - May 2013


  • hidden Markov models
  • speech synthesis
  • hidden Markov model
  • unit-selection approach
  • Hidden Markov models
  • Information processing
  • Parametric statistics
  • Speech processing
  • Speech synthesis
  • Statistical learning
  • Text processing
  • HMM-based speech synthesis system
  • Hidden Markov model (HMM)
  • statistical parametric speech synthesis
  • text-to-speech synthesis (TTS)


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