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 language | English |
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Pages (from-to) | 317-320 |
Number of pages | 4 |
Journal | IEE Proceedings I: Communications, Speech and Vision |
Volume | 140 |
Issue number | 5 |
DOIs | |
Publication status | Published - Oct 1993 |