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ABSTRACT

Native and non-native use of language differs, depending on the proficiency of the speaker, in clear and quantifiable ways. It has been shown that customizing the acoustic and language models of a natural language understanding system can significantly improve handling of non-native input; in order to make such a switch, however, the nativeness status of the user must be known. In this paper, we show that naive Bayes classification can be used to identify non-native utterances of English. The advantage of our method is that it relies on text, not on acoustic features, and can be used when the acoustic source is not available. We demonstrate that both read and spontaneous utterances can be classified with high accuracy, and that classification of errorful speech recognizer hypotheses is more accurate than classification of perfect transcriptions. We also characterize part-of-speech sequences that play a role in detecting non-native speech.

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