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Paper Abstract and Keywords
Presentation 2015-06-18 15:15
Phone Labeling Based on Gaussian Mixture Model for Dysarthric Speech Recognition
Yuki Takashima (Kobe Univ.), Toru Nakashika (UEC), Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) PRMU2015-44 SP2015-13 WIT2015-13
Abstract (in Japanese) (See Japanese page) 
(in English) We investigate in this paper speech recognition for a person with an articulation disorder resulting from athetoid cerebral palsy. As our previous work, the feature extraction method using a convolutional neural network is proposed, and showed its effectiveness. The neural network needs the teaching signal to train the network using back-propagation, and the previous method uses forced alignment using HMMs from speech data for the teaching signal. However, because the dysarthric speech fluctuates every utterance, it is difficult to obtain the correct alignment. It is considered that the network is not adequately trained due to the wrong alignment. However, phone boundaries for dysarthric speech are ambiguous, and it is difficult to give the correct alignment and it is difficult to give the correct alignment. Therefore, we propose a phone labeling method using the Gaussian distribution. In this paper, we report our experimental results of speech recognition using the networks trained by the phone alignments calculated by our proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) articulation disorders / feature extraction / convolutional neural network / bottleneck feature / phoneme labeling / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 99, SP2015-13, pp. 71-76, June 2015.
Paper # SP2015-13 
Date of Issue 2015-06-11 (PRMU, 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 PRMU2015-44 SP2015-13 WIT2015-13

Conference Information
Committee WIT SP ASJ-H PRMU  
Conference Date 2015-06-18 - 2015-06-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2015-06-WIT-SP-H-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Phone Labeling Based on Gaussian Mixture Model for Dysarthric Speech Recognition 
Sub Title (in English)  
Keyword(1) articulation disorders  
Keyword(2) feature extraction  
Keyword(3) convolutional neural network  
Keyword(4) bottleneck feature  
Keyword(5) phoneme labeling  
Keyword(6)  
Keyword(7)  
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1st Author's Name Yuki Takashima  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Toru Nakashika  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Tetsuya Takiguchi  
3rd Author's Affiliation Kobe University (Kobe Univ.)
4th Author's Name Yasuo Ariki  
4th Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2015-06-18 15:15:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # PRMU2015-44, SP2015-13, WIT2015-13 
Volume (vol) vol.115 
Number (no) no.98(PRMU), no.99(SP), no.100(WIT) 
Page pp.71-76 
#Pages 6 
Date of Issue 2015-06-11 (PRMU, SP, WIT) 

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