Improving mispronunciation detection and diagnosis of learners' speech with context-sensitive phonological rules based on language transfer

This study demonstrates how knowledge of language transfer can enable a computer-assisted pronunciation teaching (CAPT) system to effectively detect and diagnose salient mispronunciations in second language learners’ speech. Our approach uses a HMM-based speech recognizer with an extended pronunciation lexicon that includes both a model pronunciation for each word and common pronunciation variants of our target learners. The pronunciation variants in the extended pronunciation lexicon are generated based on language transfer theory (i.e knowledge from the first language is transferred to the second language). We find that a lexicon that characterizes language transfer using context-sensitive phonological rules can detect and diagnose errors better than a lexicon generated from contextinsensitive rules. Furthermore, predicting errors from language transfer alone can approach the performance of a system where the lexicon is fully-informed of all possible pronunciation errors.