Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data
Year: 2018
Type: conference-paper
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 multimodal 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.
DOI: 10.18653/v1/w18-3304
Authors: Woo Yong Choi, Kyu Ye Song, Chan Woo Lee
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W2963800675