METHOD AND APPARATUS FOR AUTOMATIC SPEECH SEGMENTATION INTO PHONEME-LIKE UNITS FOR USE IN SPEECH PROCESSING APPLICATIONS, AND BASED ON SEGMENTATION INTO BROAD PHONETIC CLASSES, SEQUENCE-CONTSTRAINED VECTOR QUANTIZATION, AND HIDDEN-MARKOV-MODELS

For machine segmenting of speech, first utterances from a database of known spoken words are classified and segmented into three broad phonetic classes (BPC) voiced, unvoiced, and silence. Next, using preliminary segmentation positions as anchor points, sequence-constrained vector quantization is used for further segmentation into phoneme-like units. Finally, exact tuning to the segmented phonemes is done through Hidden-Markov Modelling and after training a diphone set is composed for further usage.