Phoneme classification using semicontinuous hidden Markov models

Speaker-dependent phoneme recognition experiments were conducted using variants of the semicontinuous hidden Markov model (SCHMM) with explicit state duration modeling. Results clearly demonstrated that the SCHMM with state duration offers significantly improved phoneme classification accuracy compared to both the discrete HMM and the continuous HMM; the error rate was reduced by more than 30% and 20%, respectively. The use of a limited number of mixture densities significantly reduced the amount of computation. Explicit state duration modeling further reduced the error rate