  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.
