Subword-based multi-span pronunciation adaptation for recognizing accented speech

Abstract

We investigate automatic pronunciation adaptation for non-native accented speech by using statistical models trained on multi-span lingustic parse tables to generate candidate mispronunciations for a target language. Compared to traditional phone re-writing rules, parse table modeling captures more context in the form of phone-clusters or syllables, and encodes abstract features such as word-internal position or syllable structure. The proposed approach is attractive because it gives a unified method for combining multiple levels of linguistic information. The reported experiments demonstrate word error rate reductions of up to 7.9% and 3.3% absolute on Italian and German accented English using lexicon adaptation alone, and 12.4% and 11.3% absolute when combined with acoustic adaptation.