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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)446-467
Number of pages22
JournalComputer Assisted Language Learning
Volume26
Issue number5
DOIs
Publication statusPublished - 2013 Dec 1

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Efficient personalized mispronunciation detection of Taiwanese-accented English speech based on unsupervised model adaptation and dynamic sentence selection'. Together they form a unique fingerprint.

  • Cite this