Microstructural speech units and their HMM representation for discrete utterance speech recognition

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Abstract
The microsegmental modeling methods for speech recognition is extended, and a novel approach based on a phonetic feature description of microstructural characteristics of speech segments is presented. The hidden Markov model (HMM) framework is used to provide the recognition algorithm, which assumes that the underlying Markov chain tracks the evolution of a set of features related to articulatory phenomena. Use of phonetic features as the primary speech units provides a framework where the HMM structure can be designed with the guidance of detailed speech knowledge. Details of such a design are shown for a stop-consonant-vowel vocabulary. Experimental results on the task of stop-consonant discrimination demonstrate the effectiveness of this model. The error rates are reduced by over 30% compared with the conventional HMM-based recognition methods using words, phones, and microsegments as the primary speech units.

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