Improving Pronunciation Modeling for Non-Native Speech Recognition

In this paper, three different approaches to pronunciation modeling are investigated. Two existing pronunciation modeling approaches, namely the pronunciation dictionary and n-best rescoring approach are modified to work with little amount of non-native speech. We also propose a speaker clustering approach, which capable of grouping the speakers based on their pronunciation habits. Given some speech, the approach can also be used for pronunciation adaptation. This approach is called latent pronunciation analysis. The results show that conventional pronunciation dictionary perform slightly better than n-best list rescoring, while the latent pronunciation analysis has shown to be beneficial for speaker clustering, and it can produce nearly the same improvement as the pronunciation dictionary approach, without the need to know the origin of the speaker. Index Terms: non-native ASR, decision trees, n-best list rescoring, latent phonemic analysis 1