Paciic Association for Computational Linguistics Phonological Rules for Overlapping Articulatory Features in Speech Recognition

Modeling phonological units of speech is a critical issue in speech recognition. In this paper, we report our recent development of an overlapping-feature based phonological model which represents long-span contextual dependency in speech acoustics. In this model, high-level linguistic constraints are incorporated in automatic construction of the feature overlapping patterns and the associated hidden Markov model (HMM) states which represent acoustic variability. The main linguistic information explored includes morpheme and syllable boundaries, syllable constituent categories, and word stress. A consistent computational framework developed for the construction of the feature-based model and the major components of the model are described. The experimental results on use of the feature-based model as the HMM state topology for speech recognition are reported.