Segmentation of continuous speech using acoustic-phonetic parameters and statistical learning

In this paper, we present a methodology for combining acoustic-phonetic knowledge with statistical learning for automatic segmentation and classification of continuous speech. At present we focus on the recognition of broad classes-vowel, stop, fricative, sonorant consonant and silence. Judicious use is made of 13 knowledge-based acoustic parameters (APs) and support vector machines (SVMs). It has been shown earlier that SVMs perform comparable to hidden Markov models (HMMs) for detection of stop consonants. We achieve performance on segmentation of continuous speech better than the BMM based approach that uses 39 cepstrum-based speech parameters.