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Paper Abstract and Keywords
Presentation 2008-07-18 11:20
Dimensionality Reduction in Rescoring Using Likelihood Patterns Given by HMMs
Tetsuji Ogawa, Tetsunori Kobayashi (Waseda Univ.) SP2008-55
Abstract (in Japanese) (See Japanese page) 
(in English) We investigate dimensionality reduction of feature vectors in rescoring using likelihood patterns given by HMMs with long-time structures as feature parameters. The likelihood patterns calculated for word utterances
by using word-wise statistical models give discriminative patterns even if those utterances belong to the different word classes consisting of similar phonemes. This characteristic can contribute to reduction of errors for the classes that are difficult to classify by conventional ML classification in rescoring using the likelihood feature vectors with long-time structures. However, since this method utilizes the likelihood feature vectors with a dimensionality of the number of the vocabulary, it is not feasible for large vocabulary tasks. Thus, in the present paper, we attempt to reduce the dimensionality of the feature vectors by selecting only the word classes that contribute to classification from the vocabulary and using the likelihoods only for those word classes as the feature parameters. For the case in which static pattern recognition on the feature space constructed from the likelihood feature vectors is applied to rescoring of the word recognition system, proposed dimensionality reduction did not degrade the performance considerably compared to the system without dimensionality reduction, and it improved the performance compared to the conventional HMMs.
Keyword (in Japanese) (See Japanese page) 
(in English) likelihood pattern / dimensionality reduction / feature selection / rescoring / spoken word recognition / / /  
Reference Info. IEICE Tech. Rep., vol. 108, no. 142, SP2008-55, pp. 73-78, July 2008.
Paper # SP2008-55 
Date of Issue 2008-07-10 (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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Conference Information
Committee SP  
Conference Date 2008-07-17 - 2008-07-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Iwate Prefectural Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech Recognition, Speech Understanding, Speech Dialogue, etc. 
Paper Information
Registration To SP 
Conference Code 2008-07-SP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dimensionality Reduction in Rescoring Using Likelihood Patterns Given by HMMs 
Sub Title (in English)  
Keyword(1) likelihood pattern  
Keyword(2) dimensionality reduction  
Keyword(3) feature selection  
Keyword(4) rescoring  
Keyword(5) spoken word recognition  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Tetsuji Ogawa  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Tetsunori Kobayashi  
2nd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2008-07-18 11:20:00 
Presentation Time 30 minutes 
Registration for SP 
Paper # SP2008-55 
Volume (vol) vol.108 
Number (no) no.142 
Page pp.73-78 
#Pages 6 
Date of Issue 2008-07-10 (SP) 

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