通信学报2017，Vol.38Issue(12)：109-120,12.DOI:10.11959/j.issn.1000-436x.2017294
基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究
Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network
摘要
Abstract
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.关键词
多模态生理信号情感识别/栈式自编码神经网络/长短周期记忆循环神经网络/多模态生理信号融合Key words
multi-modal bio-signal emotion recognition/stacked auto-encoder neural network/LSTM recurrent neural network/multi-modal bio-signals fusion分类
信息技术与安全科学引用本文复制引用
李幼军,黄佳进,王海渊,钟宁..基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究[J].通信学报,2017,38(12):109-120,12.基金项目
国家自然科学基金资助项目(No.61420106005) （No.61420106005）
国家重点基础研究发展计划基金资助项目(No.2014CB744600) （No.2014CB744600）
国家国际科技合作专项基金资助项目(No.2013DFA32180)The National Natural Science Foundation of China (No.61420106005),The National Basic Research Program of China (No.2014CB744600),The International Science & Technology Cooperation Program of China (No.2013DFA32180) （No.2013DFA32180）
