Modelling pronunciation variation with single-path and multi-path syllable models: Issues to consider

In this paper, we construct context-independent single-path and multi-path syllable models aimed at improved pronunciation variation modelling. We use phonetic transcriptions to define the topologies of the syllable models and to initialise the model parameters, and the Baum-Welch algorithm for the re-estimation of the model parameters. We hypothesise that the richer topology of multi-path syllable models would be better at accounting for pronunciation variation than context-dependent phone models that can only account for the effects of the left and right neighbours, or single-path syllable models whose power of modelling segmental variation would seem to be limited. However, both context-dependent phone models and single-path syllable models outperform multi-path syllable models on a large vocabulary continuous speech recognition task. Careful analyses of the errors made by the recognisers with single-path and multi-path syllable models show that the most important factors affecting the speech recognition performance are syllable context and lexical confusability. In addition, the speech recognition results suggest that the benefits of the greater acoustic modelling accuracy of the multi-path syllable models can only be reaped if the information about the syllable-level pronunciation variation can be linked with the word-level information in the language model.