Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network - You-jun LI; Jia-jin HUANG; Hai-yuan WANG; Ning ZHONG (Tongxin xuebao)
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