• DocumentCode
    2705682
  • Title

    Statistical Parametric Speech Synthesis

  • Author

    Black, Alan W. ; Zen, Heiga ; Tokuda, Keiichi

  • Author_Institution
    Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA
  • Volume
    4
  • fYear
    2007
  • fDate
    15-20 April 2007
  • Abstract
    This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.
  • Keywords
    hidden Markov models; speech synthesis; HMM-based generation synthesis; statistical parametric speech synthesis; unit selection technology; Computer science; Costs; Databases; Degradation; Hidden Markov models; Loudspeakers; Natural languages; Speech synthesis; Synthesizers; Testing; Speech synthesis; hidden Markov models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
  • Conference_Location
    Honolulu, HI
  • ISSN
    1520-6149
  • Print_ISBN
    1-4244-0727-3
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2007.367298
  • Filename
    4218329