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- W4321231827 abstract "This paper presents a multimodal emotion recognition method based on dual-sequence Long Short-Term Memory (LSTM) network with attention mechanism. The ResNeXt50 network and coordinated attention mechanism are introduced to the video sequences to acquire the long-term dependence information in the location and space of video images. CNN with self-attention mechanism is utilized to catch the semantic features of audio sequences. In order to eliminate redundancy, a dual-sequence LSTM cross-modal network embedded with self-attention mechanism is applied for emotion feature fusion, and finally get the emotion output. The experimental results on RAVDESS dataset and eNTERFACE ’05 dataset prove that, compared with different baseline methods, the proposed algorithm is more attentive to the correlation between features and can significantly improve the recognition accuracy." @default.
- W4321231827 created "2023-02-18" @default.
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- W4321231827 date "2023-01-01" @default.
- W4321231827 modified "2023-09-30" @default.
- W4321231827 title "Dual-Sequence LSTM Multimodal Emotion Recognition Based on Attention Mechanism" @default.
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- W4321231827 doi "https://doi.org/10.1007/978-981-99-0301-6_12" @default.
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