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 :
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