Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network
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 fe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2017-12-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2017294/ |
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| Summary: | 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 |
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| ISSN: | 1000-436X |