Speech Emotion Recognition Based on Temporal-Spatial Learnable Graph Convolutional Neural Network

Abstract

The Graph Convolutional Neural Networks (GCN) method has shown excellent performance in the field of deep learning, and using graphs to represent speech data is a computationally efficient and scalable approach. In order to enhance the adequacy of graph neural networks in extracting speech emotional features, this paper proposes a Temporal-Spatial Learnable Graph Convolutional Neural Network (TLGCNN) for speech emotion recognition. TLGCNN firstly utilizes the Open-SMILE toolkit to extract frame-level speech emotion features. Then, a bidirectional long short-term memory (Bi LSTM) network is used to process the long-term dependencies of speech features which can further extract deep frame-level emotion features. The extracted frame-level emotion features are then input into subsequent network through two pathways. Finally, one pathway constructs the extracted frame-level deep emotion feature vectors into a graph structure applying an adaptive adjacency matrix to catch latent spatial connections, while the other pathway concatenates emotion feature vectors with graph-level embedding obtained from learnable graph convolutional neural network for prediction and classification. Through these two pathways, TLGCNN can simultaneously obtain temporal speech emotional information through Bi-LSTM and spatial speech emotional information through Learnable Graph Convolutional Neural (LGCN) network. Experimental results demonstrate that this method achieves weighted accuracy of 66.82% and 58.35% on the IEMOCAP and MSP-IMPROV databases, respectively.