The automatic recognition of stop consonants using hidden Markov models

A speaker independent technique for identifying stops in continuous speech is described. The stops are modeled with continuous hidden Markov models (CHMMs) as consisting of several well-defined segments: silence, voicing, a release and aspiration. These models are capable of performing two tasks. The first is the classification of an unknown stop and the second is to obtain a fine transcription of the stop into its segments. Features pertinent to stop recognition are obtained from the segment boundaries and are used together with the model scores in a nonparametric probability density function (PDF) estimator to identify unknown stop consonants. A recognition rate of 84% was achieved on stops occurring in vowel-stop-vowel clusters that were taken from continuous speech