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Paper Abstract and Keywords
Presentation 2019-08-28 17:00
Speech Emotion Classification based on Multi-Label Emotion Existence Estimation
Atsushi Ando, Ryo Masumura, Hosana Kamiyama, Satoshi Kobashikawa, Yushi Aono (NTT) SP2019-16
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a novel speech emotion classification that addresses the ambiguous nature of emotions in speech. Most conventional methods assume there is only a single ground truth, the dominant emotion, though utterances can contain minor emotions. These mismatch yields performance degradation. In order to solve this problem, several methods that consider ambiguous emotions~(e.g.~soft-target training) have been proposed. Unfortunately, training them is difficult since they work by estimating the proportions of all emotions. The proposed method improves both frameworks by assessing the presence or absence of each emotion before specifying the dominant. We expect that it is much easier to estimate just presence/absence of emotions rather than trying to determine proportions of each, and the deliberate assessment of emotion existence information will help to estimate the proportion of each or dominant class more precisely. The proposed method employs two-step training. Multi-Label Emotion Existence~(MLEE) model is trained first to estimate whether each emotion is present or absent. Then, the dominant emotion recognition model with hard- or soft-target labels is trained by means of the intermediate outputs of the MLEE model. Experiments demonstrate that the proposed method outperforms both hard- or soft-target based conventional emotion recognition schemes.
Keyword (in Japanese) (See Japanese page) 
(in English) emotion recognition / multi-label classification / convolutional neural network (CNN) / spectrogram / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 188, SP2019-16, pp. 39-44, Aug. 2019.
Paper # SP2019-16 
Date of Issue 2019-08-21 (SP) 
ISSN 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 2019-08-28 - 2019-08-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2019-08-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Speech Emotion Classification based on Multi-Label Emotion Existence Estimation 
Sub Title (in English)  
Keyword(1) emotion recognition  
Keyword(2) multi-label classification  
Keyword(3) convolutional neural network (CNN)  
Keyword(4) spectrogram  
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1st Author's Name Atsushi Ando  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Ryo Masumura  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Hosana Kamiyama  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Satoshi Kobashikawa  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
5th Author's Name Yushi Aono  
5th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2019-08-28 17:00:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # SP2019-16 
Volume (vol) vol.119 
Number (no) no.188 
Page pp.39-44 
#Pages
Date of Issue 2019-08-21 (SP) 


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