Mispronunciation detection leveraging maximum performance criterion training of acoustic models and decision functions

Yao Chi Hsu, Ming Han Yang, Hsiao Tsung Hung, Berlin Chen

研究成果: 雜誌貢獻會議論文同行評審

7 引文 斯高帕斯(Scopus)

摘要

Mispronunciation detection is part and parcel of a computer assisted pronunciation training (CAPT) system, facilitating second-language (L2) learners to pinpoint erroneous pronunciations in a given utterance so as to improve their spoken proficiency. This paper presents a continuation of such a general line of research and the major contributions are twofold. First, we present an effective training approach that estimates the deep neural network based acoustic models involved in the mispronunciation detection process by optimizing an objective directly linked to the ultimate evaluation metric. Second, along the same vein, two disparate logistic sigmoid based decision functions with either phone- or senone-dependent parameterization are also inferred and used for enhanced mispronunciation detection. A series of experiments on a Mandarin mispronunciation detection task seem to show the performance merits of the proposed method.

原文英語
頁(從 - 到)2646-2650
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
08-12-September-2016
DOIs
出版狀態已發佈 - 2016
事件17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, 美国
持續時間: 2016 九月 82016 九月 16

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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