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- W2804753883 abstract "Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset." @default.
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- W2804753883 date "2018-05-17" @default.
- W2804753883 modified "2023-09-27" @default.
- W2804753883 title "Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data" @default.
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