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Paper Abstract and Keywords
Presentation 2018-03-19 13:00
[Poster Presentation] Quantitative and corpus-based analysis of pronunciation diversity observed in Japanese English
Suguru Kabashima, Haoyu Zhang, Daisuke Saito, Nobuaki Minematsu (Univ. of Tokyo), Satoshi Kobashikawa, Ryo Masumura (NTT) EA2017-113 SIP2017-122 SP2017-96
Abstract (in Japanese) (See Japanese page) 
(in English) In foreign language teaching, corrective feedback to learners' pronunciation is regarded
as highly important and automatic feedback generation has been investigated using
speech technologies. For development, learners' pronunciation diversity or errors have to
be modeled adequately in advance. In this paper, based on a Japanese-English (JE) speech
corpus with detailed phonetic transcription, pronunciation diversity of JE is quantitatively
analyzed and modeled. In the corpus, read sentences, phonemic sequences that are aimed
by learners, and phonetic transcriptions of their speech are contained. Using these,
a model is built with a G2P toolkit, phonetisaurus, to predict phonemic sequences
that will be perceived by
native listeners when they listen to any words pronounced by Japanese learners.
Further, their pronunciation diversity is evaluated in terms of perplexity.
Similar investigation is possible by using pronunciation alternation rules provided
by expert teachers. The proposed model is compared to a model based on experts'
rules and it is demonstrated that the former is by far a more compact model.
Keyword (in Japanese) (See Japanese page) 
(in English) foreign language teaching / modeling pronunciation diversity / phonetic transcription / G2P / pronunciation preplexity / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 517, SP2017-96, pp. 69-74, March 2018.
Paper # SP2017-96 
Date of Issue 2018-03-12 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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)
Download PDF EA2017-113 SIP2017-122 SP2017-96

Conference Information
Committee SIP EA SP MI  
Conference Date 2018-03-19 - 2018-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, and Related Topics [SIP, EA, SP]/ Medical Image Engineering, Analysis, Recognition, etc. [MI] 
Paper Information
Registration To SP 
Conference Code 2018-03-SIP-EA-SP-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Quantitative and corpus-based analysis of pronunciation diversity observed in Japanese English 
Sub Title (in English)  
Keyword(1) foreign language teaching  
Keyword(2) modeling pronunciation diversity  
Keyword(3) phonetic transcription  
Keyword(4) G2P  
Keyword(5) pronunciation preplexity  
1st Author's Name Suguru Kabashima  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Haoyu Zhang  
2nd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
3rd Author's Name Daisuke Saito  
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 Satoshi Kobashikawa  
6th Author's Name Ryo Masumura  
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Date Time 2018-03-19 13:00:00 
Presentation Time 90 
Registration for SP 
Paper # EA2017-113, SIP2017-122, SP2017-96 
Volume (vol) 117 
Number (no) no.515(EA), no.516(SIP), no.517(SP) 
Page pp.69-74 
Date of Issue 2018-03-12 (EA, SIP, SP) 

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