HMM phoneme recognition with supervised training and Viterbi algorithm

An HMM continuous Hebrew phoneme recognition system, that requires no manual segmentation for its training was developed. A relatively small Hebrew data base was acquired for training and recognition of phonemes in continuous speech. One of the main problems in phoneme recognition, that of manual segmentation of the training data base, was overcome by a special training algorithm. The Viterbi algorithm was used in the recognition stage, and the evaluation of the results was done with the Levenshtein distance measure. Initial recognition results of Hebrew phonemes for speaker independent, text dependent cases were 69.4% correct phoneme recognition.