�ݺ�ߣ

This document discusses using graph neural networks (GNNs) for emotion and depression recognition from speech data. GNNs can capture relationships between different features in speech data represented as a graph. One paper uses a GNN with a triplet loss function to perform speech emotion recognition from variable length inputs. Another paper uses a GNN to predict depression severity based on correlations among audio features represented as a graph.