• DocumentCode
    773758
  • Title

    Bound for Minkowski metric or quadratic metric applied to VQ codeword search

  • Author

    Pan, J.-S. ; McInnes, F.R. ; Jack, M.A.

  • Author_Institution
    Centre for Commun. Interface Res., Edinburgh Univ., UK
  • Volume
    143
  • Issue
    1
  • fYear
    1996
  • fDate
    2/1/1996 12:00:00 AM
  • Firstpage
    67
  • Lastpage
    71
  • Abstract
    A bound for a Minkowski metric based on Lp distortion measure is proposed and evaluated as a means to reduce the computation in vector quantisation. This bound provides a better criterion than the absolute error inequality (AEI) elimination rule on the Euclidean distortion measure. For the Minkowski metric of order n, this bound contributes the elimination criterion from the L1 metric to L n metric. This bound can also be an extended quadratic metric which can be a hidden Markov model (HMM) with a Gaussian mixture probability density function (PDF). In speech recognition, the HMM with the Gaussian mixture VQ codebook PDF has been shown to be a promising method
  • Keywords
    Gaussian processes; hidden Markov models; probability; search problems; speech coding; speech recognition; vector quantisation; Euclidean distortion measure; Gaussian mixture probability density function; Minkowski metric bound; VQ codeword search; computation reduction; distortion measure; elimination criterion; hidden Markov model; quadratic metric bound; speech coding; speech recognition;
  • fLanguage
    English
  • Journal_Title
    Vision, Image and Signal Processing, IEE Proceedings -
  • Publisher
    iet
  • ISSN
    1350-245X
  • Type

    jour

  • DOI
    10.1049/ip-vis:19960118
  • Filename
    487848