Compact Graph Architecture for Speech Emotion Recognition

Shirian, A. and Guha, T. (2021) Compact Graph Architecture for Speech Emotion Recognition. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 6-11 June 2021, pp. 6284-6288. ISBN 9781728176055 (doi: 10.1109/ICASSP39728.2021.9413876)

Abstract

We propose a deep graph approach to address the task of speech emotion recognition. A compact, efficient and scalable way to represent data is in the form of graphs. Following the theory of graph signal processing, we propose to model speech signal as a cycle graph or a line graph. Such graph structure enables us to construct a Graph Convolution Network (GCN)-based architecture that can perform an accurate graph convolution in contrast to the approximate convolution used in standard GCNs. We evaluated the performance of our model for speech emotion recognition on the popular IEMOCAP and MSP-IMPROV databases. Our model outperforms standard GCN and other relevant deep graph architectures indicating the effectiveness of our approach. When compared with existing speech emotion recognition methods, our model achieves comparable performance to the state-of-the-art with significantly fewer learnable parameters (~30K) indicating its applicability in resource-constrained devices. Our code is available at /github.com/AmirSh15/Compact_SER.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Guha, Dr Tanaya
• Authors: Shirian, A., and Guha, T.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 2379-190X
• ISBN: 9781728176055
• Copyright Holders: Copyright © 2021 IEEE
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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