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Paper Abstract and Keywords
Presentation 2016-03-29 10:45
Tensor-based Speech Representation and its Application to Identification of Languages and Speakers
So Suzuki, Daisuke Saito, Nobuaki Minematsu (UTokyo) EA2015-127 SIP2015-176 SP2015-155
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a novel approach to speech representation for automatic identification of languages and speakers by characterizing the entire feature space by a tensor. In conventional studies of both tasks, i-vector is commonly used as the state-of-the-art representation. Here, i-vector is derived by modeling each utterance
as a GMM and projecting its supervector (GMM-SV) onto a low-dimensional space. In this paper, for the aim of explicit modeling of the correlation among mean vectors of a GMM, an utterance is not modeled as its GMM-based supervector but as its matrix and the entire set of utterances is modeled as its tensor. By projecting the tensor to a lower dimensional space, we obtain a new representation for an input utterance. We apply this method to two tasks of automatic identification of languages and speakers and evaluate its effectiveness.
Keyword (in Japanese) (See Japanese page) 
(in English) language identification / speaker identification / Gaussian mixture model / GMM supervector / i-vector / tensor analysis / Tucker decomposition /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 523, SP2015-155, pp. 341-346, March 2016.
Paper # SP2015-155 
Date of Issue 2016-03-21 (EA, SIP, 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)
Download PDF EA2015-127 SIP2015-176 SP2015-155

Conference Information
Committee EA SP SIP  
Conference Date 2016-03-28 - 2016-03-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Beppu International Convention Center B-ConPlaza 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, Speech, Signal Processing, and Related Topics 
Paper Information
Registration To SP 
Conference Code 2016-03-EA-SIP-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Tensor-based Speech Representation and its Application to Identification of Languages and Speakers 
Sub Title (in English)  
Keyword(1) language identification  
Keyword(2) speaker identification  
Keyword(3) Gaussian mixture model  
Keyword(4) GMM supervector  
Keyword(5) i-vector  
Keyword(6) tensor analysis  
Keyword(7) Tucker decomposition  
Keyword(8)  
1st Author's Name So Suzuki  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Daisuke Saito  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Nobuaki Minematsu  
3rd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2016-03-29 10:45:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # EA2015-127, SIP2015-176, SP2015-155 
Volume (vol) vol.115 
Number (no) no.521(EA), no.522(SIP), no.523(SP) 
Page pp.341-346 
#Pages
Date of Issue 2016-03-21 (EA, SIP, SP) 


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