earticle

영어

Our research aims to enhance the modeling of speech signals for more effective extraction of node features and analysis of relationships between nodes. To achieve this, we model speech signals as cyclic or linear graphs. Our model combines layers of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to leverage their respective strengths in processing graph data. Specifically, we utilize GCN to aggregate information from neighboring nodes, which helps capture local relationships among nodes. Additionally, we employ GAT mechanisms to assign varying attention weights to different neighboring nodes, facilitating a better capture of complex global relationships between nodes. In our experiments, we validate our approach using the IEMOCAP dataset and demonstrate comparable performance to state-ofthe- art models in emotion recognition tasks. This research outcome provides new insights and methodologies for further exploration in the field of speech signal processing.