Modelling Pronunciation Variation Using Multi-Path HMMs for Syllables

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MODELLING PRONUNCIATION VARIATION USING MULTI-PATH HMMS FOR SYLLABLES

Recent research suggests that it is more appropriate to model pronunciation variation with syllable-length acoustic models than with triphones. Due to the large number of factors contributing to pronunciation variation at the syllable level, the creation of multipath model topologies appears necessary. In this paper, we construct multi-path models using phonetic knowledge to initialise the parallel paths, and a data-driven solution for their reestimation. When applied to 94 frequent syllables in a Dutch read speech recognition task, the approach leads to improved recognition performance when compared with a much more complex triphone recogniser. A detailed analysis of the pronunciation variation captured by the parallel paths pinpoints the deficiencies of the approach, and provides insights into how these may be overcome. Index Terms — Speech recognition, hidden Markov models 1