The use of syllable phonotactics for word hypothesization

A search technique incorporating the automatic modeling of lexical variability is introduced for medium or large-vocabulary speaker-independent speech recognition. Current state-of-art systems depend on being able to model the entire language based on acoustic features and the constraints of syntax or interword probabilities. These methods often fail in the presence of multiple speakers, new vocabulary, noise, and spontaneous speech phenomena. A new approach for word hypothesization is proposed, based on an acoustic-phonetic unit called the pseudo-syllable segment. An algorithm is described for transforming a sequence of syllables into words. Techniques are suggested for controlling the accuracy of the syllabic hypothesis set, and learning the phonotactics of syllables automatically in a statistical framework.