• DocumentCode
    2907444
  • Title

    Vector-quantization-based speech recognition and speaker recognition techniques

  • Author

    Furui, Sadaoki

  • Author_Institution
    NTT Human Interface Lab., Tokyo, Japan
  • fYear
    1991
  • fDate
    4-6 Nov 1991
  • Firstpage
    954
  • Abstract
    The author reviews major methods of applying the vector quantization (VQ) technique to speech and speaker recognition. These include speech recognition based on the combination of VQ and the DTW/HMM (dynamic time warping/hidden Markov model) technique. VQ-distortion-based recognition, learning VQ algorithms, speaker adaptation by VQ-codebook mapping, and VQ-distortion-based speaker recognition. It is concluded that not only has the VQ technique reduced the amount of computation and storage, but it has also created new ideas for solving various problems in speech/speaker recognition
  • Keywords
    Markov processes; data compression; encoding; learning systems; speech recognition; VQ-codebook mapping; VQ-distortion-based recognition; dynamic time warping/hidden Markov model; learning VQ algorithms; speaker adaptation; speaker recognition; speech recognition; vector quantization; Hidden Markov models; Humans; Image coding; Laboratories; Nonlinear distortion; Speaker recognition; Speech coding; Speech recognition; Vector quantization; Vocabulary;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signals, Systems and Computers, 1991. 1991 Conference Record of the Twenty-Fifth Asilomar Conference on
  • Conference_Location
    Pacific Grove, CA
  • ISSN
    1058-6393
  • Print_ISBN
    0-8186-2470-1
  • Type

    conf

  • DOI
    10.1109/ACSSC.1991.186588
  • Filename
    186588