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http://hdl.handle.net/10119/16961
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| Title: | Continuous Audiovisual Emotion Recognition Using Feature Selection and LSTM |
| Authors: | Elbarougy, Reda
Atmaja, Bagus Tris
Akagi, Masato |
| Keywords: | continuous emotion recognition
audiovisual
feature selection
LSTM |
| Issue Date: | 2020-11-01 |
| Publisher: | Research Institute of Signal Processing Japan |
| Magazine name: | Journal of Signal Processing |
| Volume: | 24 |
| Number: | 6 |
| Start page: | 229 |
| End page: | 235 |
| DOI: | 10.2299/jsp.24.229 |
| Abstract: | Speech and visual information are the most dominant modalities for a human to perceive emotion. A method of recognizing human emotion from these modalities is proposed by utilizing feature selection and long short-term memory (LSTM) neural networks. A feature selection method based on support vector regression is used to select the relevant features among thousands of features extended from speech and video features via bag-of-X-words. The LSTM neural networks then are trained using a number of selected features and also separately optimized for every emotion dimension. Instead of utterance-level emotion recognition, time-frame-based processing is performed to enable continuous emotion recognition using a database labeled for each time frame. Experimental results reveal that a system with feature selection is more effective for predicting emotion dimensions for a single language than the baseline system without feature selection. The performance is measured in terms of the concordance correlation coefficient obtained by averaging the valence, arousal, and liking dimensions. |
| Rights: | Copyright (C) 2020 Research Institute of Signal Processing Japan. Reda Elbarougy, Bagus Tris Atmaja and Masato Akagi, Journal of Signal Processing, 24(6), 2020, pp.229-235. http://dx.doi.org/10.2299/jsp.24.229 |
| URI: | http://hdl.handle.net/10119/16961 |
| Material Type: | publisher |
| Appears in Collections: | b10-1. 雑誌掲載論文 (Journal Articles)
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