Abstract
The paper describes an automatic speech recognition system which is based on syllabic segmentation of the speech signal. Stochastic models (HMMs) are used for representing demisyllable segments. The advantages of syllabic processing within the different stages of the system (i.e. segmentation, phonetic classification, word and sentence recognition) are demonstrated and discussed on the basis of experimental results. Word and sentence recognition with a perplexity of 27 reached 74% and 96%, respectively.
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