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
Link To Document