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

Vaseghi, S. V., Conner, P. N. and Milner, B. P. (1993) Speech modelling using cepstral-time feature matrices in hidden Markov models. IEE Proceedings I: Communications, Speech and Vision, 140 (5). pp. 317-320. ISSN 0956-3776

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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.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: EPrints Services
Date Deposited: 01 Oct 2010 13:41
Last Modified: 15 Dec 2022 02:07
URI: https://ueaeprints.uea.ac.uk/id/eprint/2987
DOI: 10.1109/ICASSP.1994.389222

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