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Acoustic modelling of English-accented and Afrikaans-accented South African English

In this paper we investigate whether it is possible to combine speech data from two South African accents of English in order to improve speech recognition in any one accent. Our investigation is based on Afrikaans-accented English and South African English speech data. We compare three acoustic modelling approaches: separate accent-specific models, accentindependent models obtained by straightforward pooling of data across accents, and multi-accent models. For the latter approach we extend the decision-tree clustering process normally used to construct tied-state hidden Markov models by allowing accentspecific questions. We compare systems that allow such sharing between accents with those that do not. We find that accentindependent and multi-accent acoustic modelling yield similar results, both improving on accent-specific acoustic modelling.