Automatic syllabification using hierarchical hidden Markov models

This paper presents a purely statistical method for the automatic syllabification of speech. A hierarchical HMM structure is used to implement a purely acoustical model based on the phonotactic constraints found in the English language. A well-defined DTW distance measure is presented for measuring and reporting syllabification results. We achieve a token error rate of 20.3 % with a 42 ms average boundary error on a relatively large set of data. This compares well with previous knowledge- and statistically based methods.