Phone-Level Mispronunciation Detection for Computer-Assisted Language Learning

This paper presents a mispronunciation detection system which uses automatic speech recognition to effectively detect the phone-level mispronunciations in the Cantonese learners of English. Our approach extends a target pronunciation lexicon with possible phonetic confusions that may lead to pronunciation errors to generate an extended pronunciation lexicon that contains both target pronunciations for each word and pronunciation variants. The Viterbi decoding is then run with the extended pronunciation lexicon to detect phone-level mispronunciation in learners´ speech. This paper introduces a data-driven approach by performing automatic phone recognition on the Cantonese learners´ speech and analyzing the recognition errors to derive the possible phonetic confusions. The rule-based generation process leads to many implausible mispronunciations. We present a method to automatically prune the extended pronunciation lexicon. Experimental results show that the use of extended pronunciation lexicon after pruning can detect phone-level mispronunciation better than using a fully extended pronunciation lexicon.