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Paper Abstract and Keywords
Presentation 2016-07-28 14:00
Evaluation of Japanese English DNN Acoustic Models with English Level
Yuta Kawachi, Hirokazu Masataki, Taichi Asami, Yushi Aono (NTT) SP2016-20
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose an acoustic model that takes into consideration foreign language fluency level by extracting a representational vector for nativeness. Deep neural network acoustic models are commonly
used but their accuracy is not stable if they are trained using the non-native speech data. This is caused by the wide variation in non-native pronunciations, which are related to their foreign language fluency level. We tackled this problem by extending the acoustic feature vectors through the concatenation of the nativeness feature vectors. Nativeness is defined by bottleneck feature vectors of a deep neural network trained to distinguish languages. These additional feature vectors separate the input acoustic space of non-native speakers by their foreign language fluency levels to prevent confusion with features from different levels. We evaluated the proposed deep neural network acoustic model using Japanese English speech data, and show that the proposed model outperforms conventional DNN-HMM, which is trained using the same speech data.
Keyword (in Japanese) (See Japanese page) 
(in English) speech recognition / non-native / acoustic modeling / deep learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 165, SP2016-20, pp. 1-6, July 2016.
Paper # SP2016-20 
Date of Issue 2016-07-21 (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)
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Conference Information
Committee SP IPSJ-SLP  
Conference Date 2016-07-28 - 2016-07-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Takinoyu Hotel 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech recognition and understanding, dialog system, etc. 
Paper Information
Registration To SP 
Conference Code 2016-07-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of Japanese English DNN Acoustic Models with English Level 
Sub Title (in English)  
Keyword(1) speech recognition  
Keyword(2) non-native  
Keyword(3) acoustic modeling  
Keyword(4) deep learning  
1st Author's Name Yuta Kawachi  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Hirokazu Masataki  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Taichi Asami  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Yushi Aono  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Date Time 2016-07-28 14:00:00 
Presentation Time 30 minutes 
Registration for SP 
Paper # SP2016-20 
Volume (vol) vol.116 
Number (no) no.165 
Page pp.1-6 
Date of Issue 2016-07-21 (SP) 

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