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 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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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) |
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Keyword(1) |
language identification |
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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) |
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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 |
6 |
Date of Issue |
2016-03-21 (EA, SIP, SP) |
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