Speech modelling using cepstral-time feature matrices in hidden Markov models

S. V. Vaseghi, P. N. Conner, B. P. Milner

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Abstract

Conventional HMMs assume that speech spectral vectors are uncorrelated. The use of information on the temporal evolution of spectral features, within each state, can improve recognition accuracy and produce a more robust recognition system. The authors present experimental results on improvements in speech recognition using cepstral-time matrix units. Experimental evaluation using a spoken digit data base and a spoken alphabet data base, indicates that the use of cepstral-time matrix features in noisy conditions can provide an improvement in recognition of as much as 20% in comparison to a conventional spectral vector comprising of cepstral, delta cepstral and delta-delta cepstral features.
Original languageEnglish
Pages (from-to)317-320
Number of pages4
JournalIEE Proceedings I: Communications, Speech and Vision
Volume140
Issue number5
DOIs
Publication statusPublished - Oct 1993

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