Pronunciation variation modelling using accent features

In this paper, we propose a novel method for modelling native accented speech. As an alternative to the notion of dialect, we work with the lower level phonological components of accents, which we term accent features . This provides us with a better understanding of how pronunciation varies and it allows us to give a much more detailed picture of a person’s speech. The accent features are included during phonological adaptation of a speaker-independent Automatic Speech Recognition system in an attempt to make it more robust when exposed to pronunciation variation thus improving recognition performance on accented speech. We employ a dynamic set-up in which the system first identifies the phonetic characteristics of the user’s speech. It then creates a model of the speaker’s phonological system and adapts the pronunciation dictionary to best match his/her speech. Recognition is subsequently carried out using the adapted pronunciation dictionary. Experiments on British English speech data show a significant relative improvement in error rate of 20% compared with the traditional non-adaptive method.