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Paper Abstract and Keywords
Presentation 2014-02-28 11:00
Prediction of pronunciation distances based on structural representation for clustering World Englishes
Shun Kasahara, Nobuaki Minematsu (Univ. of Tokyo), HanPing Shen (NCKU), Takehiko Makino (Chuo univ.), Daisuke Saito, Keikichi Hirose (Univ. of Tokyo) SP2013-109
Abstract (in Japanese) (See Japanese page) 
(in English) The term of World Englishes is often used to indicate the current state of English as international language.It claims that English does not have the standard pronunciation and that every country, region, and even individual uses different pronunciations. From the viewpoint of World Englishes, it will be much more important to let each speaker know how his/her pronunciation is located in the diversity of World Englishes pronunciations, not how his/her pronunciation is incorrect compared to native pronunciations. This study tries to predict inter-speaker pronunciation distances only by speech analysis to examine the possibility of individual-basis pronunciation clustering of World Englishes. Speech features are often altered by non-linguistic factors such as age and gender differences. Considering this, the pronunciation structure, known as speaker-invariant feature, and support vector regression were applied for prediction. In the experiments, two conditions of a speaker-pair-open mode and a speaker-open mode were examined for training and testing the SVR. As a result, although a striking performance was obtained in the speaker-pair-open mode, only insufficient performances were found in the speaker-open mode. To predict pronunciation distances between unknown speakers, a further investigation is required.
Keyword (in Japanese) (See Japanese page) 
(in English) World Englishes / pronunciation clustering / structural representation / support vector regression / speaker-pair-open / speaker-open / /  
Reference Info. IEICE Tech. Rep., vol. 113, no. 452, SP2013-109, pp. 13-18, Feb. 2014.
Paper # SP2013-109 
Date of Issue 2014-02-21 (SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
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  
Conference Date 2014-02-28 - 2014-02-28 
Place (in Japanese) (See Japanese page) 
Place (in English) The University of Tokushima 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Pattern recognition for time sequence, etc. 
Paper Information
Registration To SP 
Conference Code 2014-02-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Prediction of pronunciation distances based on structural representation for clustering World Englishes 
Sub Title (in English)  
Keyword(1) World Englishes  
Keyword(2) pronunciation clustering  
Keyword(3) structural representation  
Keyword(4) support vector regression  
Keyword(5) speaker-pair-open  
Keyword(6) speaker-open  
Keyword(7)  
Keyword(8)  
1st Author's Name Shun Kasahara  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Nobuaki Minematsu  
2nd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
3rd Author's Name HanPing Shen  
3rd Author's Affiliation National Cheng Kung University (NCKU)
4th Author's Name Takehiko Makino  
4th Author's Affiliation Chuo university (Chuo univ.)
5th Author's Name Daisuke Saito  
5th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
6th Author's Name Keikichi Hirose  
6th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2014-02-28 11:00:00 
Presentation Time 30 minutes 
Registration for SP 
Paper # SP2013-109 
Volume (vol) vol.113 
Number (no) no.452 
Page pp.13-18 
#Pages 6 
Date of Issue 2014-02-21 (SP) 

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