计算机科学
特征学习
人工智能
编码器
模态(人机交互)
情绪识别
感知
人工神经网络
模式识别(心理学)
语音识别
机器学习
心理学
神经科学
操作系统
作者
Xiaoding Guo,Yadi Wang,Zhijun Miao,Yang Xiaojin,Jinkai Guo,Xianhong Hou,Feifei Zao
标识
DOI:10.1109/icist55546.2022.9926848
摘要
In recent years, emotion recognition technology has been widely used in emotion change perception and mental illness diagnosis. Previous methods are mainly based on single-task learning strategies, which are unable to fuse multimodal features and remove redundant information. This paper proposes an emotion recognition model ER-MRL, which is based on multimodal representation learning. ER-MRL vectorizes the multimodal emotion data through encoders based on neural networks. The gate mechanism is used for multimodal feature selection. On this basis, ER-MRL calculates the modality specific and modality invariant representation for each emotion category. The Transformer model and multihead self-attention layer are applied to multimodal feature fusion. ER-MRL figures out the prediction result through the tower layer based on fully connected neural networks. Experimental results on the CMU-MOSI dataset show that ER-MRL has better performance on emotion recognition than previous methods.
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