Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data
Computation and Language
2019-03-11 v2 Artificial Intelligence
Human-Computer Interaction
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.
Cite
@article{arxiv.1805.06606,
title = {Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data},
author = {Chan Woo Lee and Kyu Ye Song and Jihoon Jeong and Woo Yong Choi},
journal= {arXiv preprint arXiv:1805.06606},
year = {2019}
}
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