• DocumentCode
    1150019
  • Title

    TechWare: HMM-based speech synthesis resources [Best of the Web]

  • Author

    Zen, Heiga ; Tokuda, Keiichi

  • Author_Institution
    Speech Technol. Group, Toshiba Res. Eur. Ltd., Cambridge, UK
  • Volume
    26
  • Issue
    4
  • fYear
    2009
  • fDate
    7/1/2009 12:00:00 AM
  • Firstpage
    95
  • Lastpage
    97
  • Abstract
    This paper focuses on hidden Markov model (HMM)- based speech synthesis, which has recently been demonstrated to be very effective in generating high-quality speech and started dominating speech synthesis research. The attractive point of this approach is that the synthesized speech can easily be modified by transforming HMM parameters with a small amount of speech data. Thus it is very useful for constructing speech synthesizers with various voice characteristics, speaking styles, and emotions.
  • Keywords
    hidden Markov models; speech synthesis; HMM-based speech synthesis; emotion; hidden Markov model; speaking style; speech data; voice characteristics; Databases; Degradation; Hidden Markov models; History; Interpolation; Maximum likelihood estimation; Parameter estimation; Speech synthesis; Synthesizers; Wikipedia;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1053-5888
  • Type

    jour

  • DOI
    10.1109/MSP.2009.932563
  • Filename
    5174504