Efficient personalized mispronunciation detection of Taiwanese-accented English speech based on unsupervised model adaptation and dynamic sentence selection

Chung Hsien Wu, Hung Yu Su, Chao Hong Liu

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

This study presents an efficient approach to personalized mispronunciation detection of Taiwanese-accented English. The main goal of this study was to detect frequently occurring mispronunciation patterns of Taiwanese-accented English instead of scoring English pronunciations directly. The proposed approach quickly identifies personalized mispronunciations of students, enabling English teachers to spend more time on teaching or rectifying student pronunciations. In this approach, an unsupervised model adaptation method was performed on the universal acoustic models to recognize the speech of a specific speaker with mispronunciations and a Taiwanese accent. A dynamic sentence selection algorithm that considers the mutual information of the related mispronunciations is proposed to select a sentence containing the most undetected mispronunciations to quickly detect personalized mispronunciations. The experimental results show that the proposed unsupervised adaptation approach obtains an accuracy improvement of approximately 2.1% in the recognition of Taiwanese-accented English speech.

原文English
頁(從 - 到)446-467
頁數22
期刊Computer Assisted Language Learning
26
發行號5
DOIs
出版狀態Published - 2013 十二月

All Science Journal Classification (ASJC) codes

  • 語言與語言學
  • 語言和語言學
  • 電腦科學應用

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