Pronunciation variant-based multi-path HMMs for syllables

Recent research suggests that it is more appropriate to model pronunciation variation with syllable-length acoustic models than with context-dependent phones. Due to the large number of factors contributing to pronunciation variation at the syllable level, the creation of multi-path model topologies appears necessary. In this paper, we propose a novel approach for constructing multi-path models for frequent syllables. The suggested approach uses phonetic knowledge for the initialisation of the parallel paths, and a data-driven solution for their re-estimation. When applied to 94 frequent syllables in a 37-hour corpus of Dutch read speech, it leads to improved recognition performance when compared with a triphone recogniser of similar complexity.