【摘要】 为了提高情感识别的分类准确率,提出一种将栈式自编码神经网络(SAE)和长短周期记忆单元循环神经网络(LSTM RNN)融合的多模态融合特征情感识别方法。该方法通过SAE对不同模态的生理特征进行信息融合和压缩,随后用LSTM RNN对长时间周期的融合进行情感分类识别。通过将该方法用到开源数据集中进行验证,得到情感分类准确率达到0.792 6。实验结果表明,SAE对多模态生理特征进行了有效融合,LSTM RNN能够有效地对长时间周期中的关键特征进行识别。更多还原

【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更多还原