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 multi-path 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 re-estimation. 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.