Representation Learning with Graph Neural Networks for Speech Emotion Recognition
Abstract
Learning expressive representation is crucial in deep learning. In speech emotion recognition (SER), vacuum regions or noises in the speech interfere with expressive representation learning. However, traditional RNN-based models are susceptible to such noise. Recently, Graph Neural Network (GNN) has demonstrated its effectiveness for representation learning, and we adopt this framework for SER. In particular, we propose a cosine similarity-based graph as an ideal graph structure for representation learning in SER. We present a Cosine similarity-based Graph Convolutional Network (CoGCN) that is robust to perturbation and noise. Experimental results show that our method outperforms state-of-the-art methods or provides competitive results with a significant model size reduction with only 1/30 parameters.
Cite
@article{arxiv.2208.09830,
title = {Representation Learning with Graph Neural Networks for Speech Emotion Recognition},
author = {Junghun Kim and Jihie Kim},
journal= {arXiv preprint arXiv:2208.09830},
year = {2022}
}
Comments
AAAI 2022 Workshop on Graphs and More Complex Structures for Learning and Reasoning (GCLR)