Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network

被引：0
|
作者：
Li Y.-J. [1 ,2 ,3 ]
Huang J.-J. [1 ,2 ,3 ]
Wang H.-Y. [1 ,2 ,3 ]
Zhong N. [1 ,2 ,3 ,4 ]
机构：
[1] Institute of International WIC, Beijing University of Technology, Beijing
[2] Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing
[3] Beijing International Collaboration Base on Brain Informatics Wisdom and Services, Beijing
[4] Beijing Advanced Innovation Center for Future Internet Technology, Beijing
来源：
Tongxin Xuebao/Journal on Communications | 2017年 / 38卷 / 12期
基金：
对外科技合作项目（国际科技项目）; 中国国家自然科学基金;
关键词：
LSTM recurrent neural network; Multi-modal bio-signal emotion recognition; Multi-modal bio-signals fusion; Stacked auto-encoder neural network;
D O I：
10.11959/j.issn.1000-436x.2017294
中图分类号：
学科分类号：
摘要：
In order to achieve more accurate emotion recognition accuracy from multi-modal bio-signal features, a novel method to extract and fuse the signal with the stacked auto-encoder and LSTM recurrent neural networks was proposed. The stacked auto-encoder neural network was used to compress and fuse the features. The deep LSTM recurrent neural network was employed to classify the emotion states. The results present that the fused multi-modal features provide more useful information than single-modal features. The deep LSTM recurrent neural network achieves more accurate emotion classification results than other method. The highest accuracy rate is 0.792 6 © 2017, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：109 / 120
页数：11
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