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Paper Abstract and Keywords
Presentation 2019-10-26 17:00
Neural Whispered Speech Detection with Imbalanced Learning
Takanori Ashihara, Yusuke Shinohara, Hiroshi Sato, Takafumi Moriya, Kiyoaki Matsui, Yoshikazu Yamaguchi (NTT) SP2019-26 WIT2019-25
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we present a neural whispered-speech detection technique that offers utterance-level classification of whispered and non-whispered speech exhibiting imbalanced data distributions.
Previous studies have shown that machine learning models trained on a large amount of whispered and non-whispered utterances perform remarkably well for whispered speech detection.
However, it is often difficult to collect large numbers of whispered utterances.
In this paper, we propose a method to train neural whispered speech detectors from a small amount of whispered utterances in combination with a large amount of non-whispered utterances.
In doing so, special care is taken to ensure that severely imbalanced datasets can effectively train neural networks.
Specifically, we use a class-aware sampling method for training neural networks.
To evaluate the networks, we gather test samples recorded by both condenser and smartphone microphones at different distances from the speakers to simulate practical environments.
Experiments show the importance of imbalanced learning in enhancing the performance of utterance level classifiers.
Keyword (in Japanese) (See Japanese page) 
(in English) whispered speech / vocal effort / deep neural networks / imbalanced learning / class-aware sampling / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 250, SP2019-26, pp. 51-56, Oct. 2019.
Paper # SP2019-26 
Date of Issue 2019-10-19 (SP, WIT) 
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)
Download PDF SP2019-26 WIT2019-25

Conference Information
Committee WIT SP  
Conference Date 2019-10-26 - 2019-10-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Daiichi Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2019-10-WIT-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Neural Whispered Speech Detection with Imbalanced Learning 
Sub Title (in English)  
Keyword(1) whispered speech  
Keyword(2) vocal effort  
Keyword(3) deep neural networks  
Keyword(4) imbalanced learning  
Keyword(5) class-aware sampling  
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1st Author's Name Takanori Ashihara  
1st Author's Affiliation NTT Corporation (NTT)
2nd Author's Name Yusuke Shinohara  
2nd Author's Affiliation NTT Corporation (NTT)
3rd Author's Name Hiroshi Sato  
3rd Author's Affiliation NTT Corporation (NTT)
4th Author's Name Takafumi Moriya  
4th Author's Affiliation NTT Corporation (NTT)
5th Author's Name Kiyoaki Matsui  
5th Author's Affiliation NTT Corporation (NTT)
6th Author's Name Yoshikazu Yamaguchi  
6th Author's Affiliation NTT Corporation (NTT)
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Speaker Author-1 
Date Time 2019-10-26 17:00:00 
Presentation Time 20 minutes 
Registration for SP 
Paper # SP2019-26, WIT2019-25 
Volume (vol) vol.119 
Number (no) no.250(SP), no.251(WIT) 
Page pp.51-56 
#Pages
Date of Issue 2019-10-19 (SP, WIT) 


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