Acoustic Model Adaptation Based on Pronunciation Variability Analysis for Non-Native Speech Recognition

Abstract

In this paper, we investigate the pronunciation variability between native and non-native speakers and propose an acoustic model adaptation method based on the variability analysis in order to improve the performance of a non-native speech recognition system. The proposed acoustic model adaptation is performed in two steps. First, we construct baseline acoustic models from native speech, and perform phone recognition by using the baseline acoustic models to identify most informative variant phonetic units from native to non-native. Next, the acoustic model corresponding to each informative variant phonetic unit is adapted so that the state tying of the acoustic model for non-native speech reflects such a phonetic variability. For further improvement, the traditional acoustic model adaptation such as MLLR or MAP could be applied on the system that is adapted with the proposed method. In this work, we select English as a target language and non-native speakers are all Korean. It is shown from the continuous Korean-English speech recognition experiments that the proposed method can achieve the average word error rate reduction by 12.75% when compared with the speech recognition system with the baseline acoustic models trained by native speech. Moreover, the reduction of 57.12% in the average word error rate is obtained by applying MLLR or MAP adaptation to the adapted acoustic models by the proposed method.