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Paper Abstract and Keywords
Presentation 2022-08-26 11:42
Study on Relationship Between Speakers' Physiological Structure and Acoustic Speech Signals: Data-Driven Study Based on Frequency-Wise Attentional Neural Network
Li Kai (JAIST), Xugang Lu (NICT), Masato Akagi, Jianwu Dang (JAIST), Sheng Li (NICT), Unoki Masashi (JAIST) SIP2022-68
Abstract (in Japanese) (See Japanese page) 
(in English) Quantitatively revealing the relationship between speakers’ physiological structure and acoustic speech signals by considering the properties of resonance and antiresonance can help us to extract effective speaker discriminative information (SDI) from speech signals. The conventional quantification method based on F-ratio only considers the power of acoustic speech in each frequency band independently. We propose a novel frequency-wise attentional neural network to learn the nonlinear combined effect of the frequency components on speaker identity. The learned results indicate that antiresonance frequency induced by the nasal cavity is another essential factor
for speaker discrimination that the F-ratio method could not reveal. To further evaluate our findings, we designed a non-uniform subband processing strategy based on the learned results for speaker feature extraction and did automatic speaker verification (ASV). The ASV results confirmed that further emphasizing the spectral structure
around the antiresonance frequency region can enhance speaker discrimination.
Keyword (in Japanese) (See Japanese page) 
(in English) physiological feature / non-uniform filterbank / frequency-wise attention / data-driven feature / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 165, SIP2022-68, pp. 97-102, Aug. 2022.
Paper # SIP2022-68 
Date of Issue 2022-08-18 (SIP) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 SIP  
Conference Date 2022-08-25 - 2022-08-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Nobumoto Ohama Memorial Hall (Ishigaki Island) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Signal processing, etc. 
Paper Information
Registration To SIP 
Conference Code 2022-08-SIP 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Relationship Between Speakers' Physiological Structure and Acoustic Speech Signals: Data-Driven Study Based on Frequency-Wise Attentional Neural Network 
Sub Title (in English)  
Keyword(1) physiological feature  
Keyword(2) non-uniform filterbank  
Keyword(3) frequency-wise attention  
Keyword(4) data-driven feature  
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1st Author's Name Li Kai  
1st Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
2nd Author's Name Xugang Lu  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
3rd Author's Name Masato Akagi  
3rd Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
4th Author's Name Jianwu Dang  
4th Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
5th Author's Name Sheng Li  
5th Author's Affiliation National Institute of Information and Communications Technology (NICT)
6th Author's Name Unoki Masashi  
6th Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
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Speaker Author-1 
Date Time 2022-08-26 11:42:00 
Presentation Time 18 minutes 
Registration for SIP 
Paper # SIP2022-68 
Volume (vol) vol.122 
Number (no) no.165 
Page pp.97-102 
#Pages 6 
Date of Issue 2022-08-18 (SIP) 

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