Enhanced time duration constraints in hidden Markov modelling for phoneme recognition

The use of enhanced time duration constraints for subword (phoneme) recognition in continuous speech is reported. Here the time duration constraints are modelled by a Gaussian probability distribution in the conventional Baum-Welch learning algorithm and are statistically enhanced to obtain the most probable path in the Viterbi decoding process. Experimental results to validate this approach are included.