NDLI: Automatic Chinese pronunciation error detection using SVM trained with structural features

Content Provider	IEEE Xplore Digital Library
Author	Tongmu Zhao Hoshino, A. Suzuki, M. Minematsu, N. Hirose, K.
Copyright Year	2012
Description	Author affiliation: University of Tokyo (Tongmu Zhao; Suzuki, M.; Minematsu, N.; Hirose, K.) \|\| Toyama National College of Technology (Hoshino, A.)
Abstract	Pronunciation errors are often made by learners of a foreign language. To build a Computer-Assisted Language Learning (CALL) system to support them, automatic error detection is essential. In this study, Japanese learners of Chinese are focused on. We investigated in automatic detection of their typical and frequent phoneme production errors. For this aim, four databases are newly created and we propose a detection method using Support Vector Machine (SVM) with structural features. The proposed method is compared to two baseline methods of Goodness Of Pronunciation (GOP) and Likelihood Ratio (LR) under the task of phoneme error detection. Experiments show that the proposed method performs much better than both of the two baseline methods. For example, the false rejection rate is reduced by as much as 82%. However, the results also indicate some drawbacks of using SVM with structural features. In this paper, we discuss merits and demerits of the proposed method and in what kind of real applications it works effectively.
Starting Page	473
Ending Page	478
File Size	469577
Page Count	6
File Format	PDF
ISBN	9781467351256
e-ISBN	9781467351263
e-ISBN	9781467351249
DOI	10.1109/SLT.2012.6424270
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pronunciation error detection LR robustness GOP SVM Support vector machines Training Databases Hidden Markov models structural feature Feature extraction Speech Chinese Testing
Content Type	Text
Resource Type	Article

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