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.
|Number of pages||4|
|Journal||IEE Proceedings I: Communications, Speech and Vision|
|Publication status||Published - Oct 1993|