Non-native speech differs significantly from native speech, often resulting in a degradation of the performance of automatic speech recognition (ASR). Hand-crafted pronunciation lexicons used in standard ASR systems generally fail to cover non-native pronunciations, and design of new ones by linguistic experts is time consuming and costly. In this work, we propose acoustic data-driven iterative pronunciation learning for non-native speech recognition, which automatically learns non-native pronunciations directly from speech using an iterative estimation procedure. Grapheme-to-Phoneme (G2P) conversion is used to predict multiple candidate pronunciations for each word, occurrence frequency of pronunciation variations is estimated from the acoustic data of non-native speakers, and these automatically estimated pronunciation variations are used to perform acoustic model adaptation. We investigate various cases such as learning (1) without knowledge of non-native pronunciation, and (2) when we adapt to the speaker's proficiency level. In experiments on speech from non-native speakers of various levels, the proposed method was able to achieve an 8.9% average improvement in accuracy.
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