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Paper Abstract and Keywords
Presentation 2012-07-19 14:30
Automatic pronunciation error detecting of Chinese using SVM with structural features
Tongmu Zhao (Univ. of Tokyo), Akemi Hoshino (Toyama National College of Tech.), Masayuki Suzuki, Nobuaki Minematsu, Keikichi Hirose (Univ. of Tokyo) SP2012-50
Abstract (in Japanese) (See Japanese page) 
(in English) Pronunciation errors are often made by learners of a foreign language. To build a CALL (Computer-Aided Language Learning) system to support them, automatic error detection is an essential technique. In this study, Japanese learners of Chinese are focused on and automatic detection of their typical and frequent phoneme production errors is investigated. Due to difficulty of preparing labels of the phoneme errors found in real learners' data, we prepare native utterances including artificial phoneme errors. First, the target phonemes, which often appear to be problematic for Japanese learners to pronounce correctly, are defined through discussion with teachers. Then, phoneme production errors are created artificially by changing transcripts of native utterances and we further ask native speakers to read sentences with specific phoneme production errors. Three methods of GOP (Goodness of Pronunciation), LR (Likelihood Ratio), and SVM (Support Vector Machine) with struc-tural features are compared under the task of phoneme error detection. Here, GOP-based error detection is done by using the threshold optimized through development data. Results show that SVM with structural features performs better than both of the GOP-based and LR-based baseline methods.
Keyword (in Japanese) (See Japanese page) 
(in English) CALL / phoneme errors / error detection / Chinese / SVM / structural feature / GOP / likelihood ratio  
Reference Info. IEICE Tech. Rep., vol. 112, no. 141, SP2012-50, pp. 1-6, July 2012.
Paper # SP2012-50 
Date of Issue 2012-07-12 (SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SP IPSJ-SLP  
Conference Date 2012-07-19 - 2012-07-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Hotel Takinoyu (Yamagata Pref.) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech recognition, understanding, dialog, etc. 
Paper Information
Registration To SP 
Conference Code 2012-07-SP-SLP 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic pronunciation error detecting of Chinese using SVM with structural features 
Sub Title (in English)  
Keyword(1) CALL  
Keyword(2) phoneme errors  
Keyword(3) error detection  
Keyword(4) Chinese  
Keyword(5) SVM  
Keyword(6) structural feature  
Keyword(7) GOP  
Keyword(8) likelihood ratio  
1st Author's Name Tongmu Zhao  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Akemi Hoshino  
2nd Author's Affiliation Toyama National College of Technology (Toyama National College of Tech.)
3rd Author's Name Masayuki Suzuki  
3rd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
4th Author's Name Nobuaki Minematsu  
4th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
5th Author's Name Keikichi Hirose  
5th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2012-07-19 14:30:00 
Presentation Time 30 minutes 
Registration for SP 
Paper # SP2012-50 
Volume (vol) vol.112 
Number (no) no.141 
Page pp.1-6 
#Pages
Date of Issue 2012-07-12 (SP) 


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