Phonetic Segmentation and Labeling Based on a Hidden Markov Model

It is generally accepted that speech is and acoustic manifestation of an underlying phonetic code having a relatively few symbols. The code is, however, a purely mental representation of the spoken language and, as such, is not directly observable. Since the hidden Markov model comprises an unobservable Markov chain and a set of random processes that can be directly measured, it seems most natural to represent speech as a hidden Markov chain in which the hidden states correspond to the putative unobservable phonetic symbols and the state-dependent random processes account for the variability of the observable acoustic manifestation of the corresponding phonetic symbol.