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Paper Abstract and Keywords
Presentation 2014-05-24 11:30
Discriminative training of acoustic models for system combination
Yuuki Tachioka (Mitsubishi Electric), Shinji Watanabe, Jonathan Le Roux, John R. Hershey (MERL) SP2014-15
Abstract (in Japanese) (See Japanese page) 
(in English) In discriminative training methods, the objective function is designed to improve the performance of automatic speech recognition with reference to correct labels using a single system. On the other hand, system combination methods, which output refined hypotheses by a majority voting scheme, need to build multiple systems that generate complementary hypotheses. This paper aims to unify the both requirements within a discriminative training framework based on the mutual information criterion. That is, we construct complementary models by optimizing the proposed objective function, which yields to minimize the mutual information with base systems' hypotheses, while maximize that with correct labels, at the same time. We also analyze that this scheme corresponds to weight the training data of a complementary system by considering correct and error tendencies in the base systems, which has close relationship with boosting methods. In addition, the proposed method can practically construct complementary systems by simply extending a lattice-based parameter update algorithm in discriminative training, and can adjust the degree of how much the complementary system outputs are different from base system ones. The experiments on highly noisy speech recognition (`The 2nd CHiME challenge') show the effectiveness of the proposed method, compared with a conventional system combination approach.
Keyword (in Japanese) (See Japanese page) 
(in English) Discriminative training / Margin training / Boosting / System combination / MMI / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 52, SP2014-15, pp. 147-152, May 2014.
Paper # SP2014-15 
Date of Issue 2014-05-17 (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-MUS  
Conference Date 2014-05-24 - 2014-05-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2014-05-SP-MUS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Discriminative training of acoustic models for system combination 
Sub Title (in English)  
Keyword(1) Discriminative training  
Keyword(2) Margin training  
Keyword(3) Boosting  
Keyword(4) System combination  
Keyword(5) MMI  
1st Author's Name Yuuki Tachioka  
1st Author's Affiliation Mitsubishi Electric (Mitsubishi Electric)
2nd Author's Name Shinji Watanabe  
2nd Author's Affiliation Mitsubishi Electric Research Labs (MERL)
3rd Author's Name Jonathan Le Roux  
3rd Author's Affiliation Mitsubishi Electric Research Labs (MERL)
4th Author's Name John R. Hershey  
4th Author's Affiliation Mitsubishi Electric Research Labs (MERL)
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Date Time 2014-05-24 11:30:00 
Presentation Time 240 
Registration for SP 
Paper # IEICE-SP2014-15 
Volume (vol) IEICE-114 
Number (no) no.52 
Page pp.147-152 
#Pages IEICE-6 
Date of Issue IEICE-SP-2014-05-17 

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