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Paper Abstract and Keywords
Presentation 2021-07-16 13:50
Inverse esitimaion of shapes of vocal-tract models with cascading two acoustic tubes from sound spectrogram using CNN
Takuya Chiba, Hiroki Matsuzaki, Naofumi Wada, Megumi Takezawa, Hirofumi Sanada (Hokkaido Univ of Science) EA2021-19
Abstract (in Japanese) (See Japanese page) 
(in English) We are attempting to use machine learning to vocal tract shape from speaking voice. For this purpose, we have used the vocal tract area function as the output and the vocal tract transfer function as the input as the training data, and have attempted inverse estimation using a neural network consisting of multiple fully connected layers, but have not been able to obtain sufficient estimation accuracy. Another problem was that the voice data itself was not used for training. In this study, we used a convolutional neural network (CNN), which has been widely used in image processing, as the input data to obtain a sound spectrogram from speaking voice. InceptionV3, VGG16, and ResNet50, which are often used in classification problems, were used as CNNs after changing the activation function used in the output layer from a softmax function to an equality function to fit the regression problem of this study. As a result, we were not able to obtain high accuracy with this implementation method for any of the CNN models.
Keyword (in Japanese) (See Japanese page) 
(in English) Sound Spectrogrum / Vocal Tract Area / Inverse Estimation / CNN / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 112, EA2021-19, pp. 89-94, July 2021.
Paper # EA2021-19 
Date of Issue 2021-07-08 (EA) 
ISSN Online edition: ISSN 2432-6380
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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 EA ASJ-H  
Conference Date 2021-07-15 - 2021-07-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, Psychological and Physiological Acoustics, Speech, Musical Acoustics, Education in Acoustics, and Related Topics 
Paper Information
Registration To EA 
Conference Code 2021-07-EA-H 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Inverse esitimaion of shapes of vocal-tract models with cascading two acoustic tubes from sound spectrogram using CNN 
Sub Title (in English)  
Keyword(1) Sound Spectrogrum  
Keyword(2) Vocal Tract Area  
Keyword(3) Inverse Estimation  
Keyword(4) CNN  
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1st Author's Name Takuya Chiba  
1st Author's Affiliation Hokkaido University of Science (Hokkaido Univ of Science)
2nd Author's Name Hiroki Matsuzaki  
2nd Author's Affiliation Hokkaido University of Science (Hokkaido Univ of Science)
3rd Author's Name Naofumi Wada  
3rd Author's Affiliation Hokkaido University of Science (Hokkaido Univ of Science)
4th Author's Name Megumi Takezawa  
4th Author's Affiliation Hokkaido University of Science (Hokkaido Univ of Science)
5th Author's Name Hirofumi Sanada  
5th Author's Affiliation Hokkaido University of Science (Hokkaido Univ of Science)
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Speaker Author-1 
Date Time 2021-07-16 13:50:00 
Presentation Time 25 minutes 
Registration for EA 
Paper # EA2021-19 
Volume (vol) vol.121 
Number (no) no.112 
Page pp.89-94 
#Pages
Date of Issue 2021-07-08 (EA) 


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