Paper Abstract and Keywords |
Presentation |
2019-12-19 14:00
Mathematical Representation of Emotion by Combining Recognition and Unification Tasks Using Multimodal Deep Neural Networks Seiichi Harata, Takuto Sakuma, Shohei Kato (NITech) HIP2019-65 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
To emulate human emotions in robots, the mathematical representation of emotion is important for all components of affective computing such as emotion recognition, generation, and expression. There are several methods to represent emotions by vectors of continuous values and mapping them from uni-modality data to low-dimensional space. However, the representation of emotions obtained by uni-modality data seems to depend on such modality. In this study, we proposed integrating multi-modalities on a DNN acquiring mathematical representation (emotional space) of emotion. We aim at the acquisition of emotional space which does not depend on modalities by combining recognition task and unification task. Experiments with audio-visual data have confirmed two things. First, there are differences in the emotional space acquired from a single modality. Second, the proposed method can acquire a modality independent emotional space. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Affective Computing / Deep Neural Networks / Multi-modal / Multi-task Learning / Metric Learning / Emotional Space / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 348, HIP2019-65, pp. 1-6, Dec. 2019. |
Paper # |
HIP2019-65 |
Date of Issue |
2019-12-12 (HIP) |
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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HIP2019-65 |
Conference Information |
Committee |
HIP |
Conference Date |
2019-12-19 - 2019-12-20 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
RIEC, Tohoku University |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
HIP |
Conference Code |
2019-12-HIP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Mathematical Representation of Emotion by Combining Recognition and Unification Tasks Using Multimodal Deep Neural Networks |
Sub Title (in English) |
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Keyword(1) |
Affective Computing |
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Deep Neural Networks |
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Multi-modal |
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Multi-task Learning |
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Metric Learning |
Keyword(6) |
Emotional Space |
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1st Author's Name |
Seiichi Harata |
1st Author's Affiliation |
Nagoya Institute of Technology (NITech) |
2nd Author's Name |
Takuto Sakuma |
2nd Author's Affiliation |
Nagoya Institute of Technology (NITech) |
3rd Author's Name |
Shohei Kato |
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Nagoya Institute of Technology (NITech) |
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Speaker |
Author-1 |
Date Time |
2019-12-19 14:00:00 |
Presentation Time |
30 minutes |
Registration for |
HIP |
Paper # |
HIP2019-65 |
Volume (vol) |
vol.119 |
Number (no) |
no.348 |
Page |
pp.1-6 |
#Pages |
6 |
Date of Issue |
2019-12-12 (HIP) |
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