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 inter-word 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.