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Paper Abstract and Keywords
Presentation 2021-03-03 16:45
An optimal prediction of phoneme under Bayes criterion by weighting multiple hidden Markov models
Taishi Yamaoka, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) EA2020-76 SIP2020-107 SP2020-41
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a prediction method for prediction problems using a hidden Markov model. Specifically, it is a proposal for phoneme recognition, which is one of the prediction problems. In the previous studies on phoneme recognition using the Hidden Markov Model, the Hidden Markov Model used for prediction is defined as one by a certain criteria. In addition, for the defined Hidden Markov Model, parameters were estimated from the training data, and the phonemes corresponding to the new speech data were predicted using paremters.
In this peper, we assume 0-1 loss as the loss function, and formulate the optimum prediction based on Bayesian criterion. In other words, instead of selecting one Hidden Markov Model and estimating its parameters and making predictions using them, we propose a prediction that directly minimizes the probability of error in the prediction.
Although this prediction is theoretically optimal, its calculation involves two problems: (i) The complexity of the sum calculation of the state transition series is on the exponential order with respect to the length of the voice. (ii) It is difficult to analytically calculate the integral by the posterior distribution of the parameters of the Hidden Markov Model. In order to solve these problems, in this paper, we apply the Viterbi algorithm for problem (i) and the Variational Bayesian method for problem (ii), and propose a Bayesian semi-optimal algorithm. This algorithm makes predictions by weighted averages of approximate posterior distributions of multiple Hidden Markov Models. By conducting numerical experiments using artificial data, it was confirmed that the proposed method has a smaller false recognition rate than the method of selecting and predicting one model as in the previous research.
Keyword (in Japanese) (See Japanese page) 
(in English) Phoneme recognition / Hidden Markov model / Bayes criteria / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 399, SP2020-41, pp. 97-102, March 2021.
Paper # SP2020-41 
Date of Issue 2021-02-24 (EA, SIP, 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)
Download PDF EA2020-76 SIP2020-107 SP2020-41

Conference Information
Committee EA US SP SIP IPSJ-SLP  
Conference Date 2021-03-03 - 2021-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech, Engineering/Electro Acoustics, Signal Processing, Ultrasonics, and Related Topics 
Paper Information
Registration To SP 
Conference Code 2021-03-EA-US-SP-SIP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An optimal prediction of phoneme under Bayes criterion by weighting multiple hidden Markov models 
Sub Title (in English)  
Keyword(1) Phoneme recognition  
Keyword(2) Hidden Markov model  
Keyword(3) Bayes criteria  
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1st Author's Name Taishi Yamaoka  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Shota Saito  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Toshiyasu Matsushima  
3rd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2021-03-03 16:45:00 
Presentation Time 25 minutes 
Registration for SP 
Paper # EA2020-76, SIP2020-107, SP2020-41 
Volume (vol) vol.120 
Number (no) no.397(EA), no.398(SIP), no.399(SP) 
Page pp.97-102 
#Pages 6 
Date of Issue 2021-02-24 (EA, SIP, SP) 

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