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Hierarchical phoneme recognition by hidden Markov models based on multiple feature integration

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3 Author(s)
Y. Ariki ; Edinburgh Univ., UK ; F. R. McInnes ; M. A. Jack

A method of hierarchical phoneme recognition which utilises the most selective features for each individual phoneme is reported. Input speech patterns are classified into broad classes on the basis of LPC-derived cepstral data. Then, the speech is further classified to a fine-class level using mel-formant data for vowel models only. Hidden Markov models (HMM) are used at both levels of classification.

Published in:

Electronics Letters  (Volume:25 ,  Issue: 14 )