A speaker independent technique for identifying stops in continuous speech is described. The stops are modelled with continuous hidden Markov models (CHMM's) 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 non-parametric probability density function (pdf) estimator to identify the unknown stop consonants. A recognition rate of 84% was achieved on stops occurring in vowel-stop-vowel clusters that were taken from continuous speech.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.