A Novel Framework for Emotion Detection with Graph Neural Networks: A Deep Learning Perspective

Speech emotion detection is a critical task in human-computer interaction, with applications ranging from customer service to mental health monitoring. This work explores the potential of Graph Neural Networks (GNNs) for capturing the intricate temporal and relational patterns in speech signals, offering an innovative approach to emotion recognition. By transforming audio features into graph representations, the proposed method models both local feature dependencies and global temporal relationships. The methodology was evaluated on four benchmark datasets: RAVDESS, CREMA-D, TESS, and SAVEE, showcasing its robustness across diverse speaker profiles and emotional expressions. Key audio features, including Mel Spectrograms, MFCCs, and Chroma features, were extracted using the Librosa library. Graph-based representations were constructed where nodes represented overlapping audio segments, and edges captured temporal and feature-level relationships. A custom GNN architecture, featuring Graph Convolutional Layers, attention mechanisms, and global pooling, was implemented to process these representations. The model achieved an accuracy of 88%, outperforming baseline CNN and RNN models by 5-10%. This study highlights the advantages of graph-based methods for emotion detection, including improved feature learning, scalability, and interpretability. Future directions include integrating real-time processing capabilities, exploring multi-modal approaches combining speech with visual and textual data, and optimizing graph construction for large-scale datasets. The findings underscore the potential of GNNs to redefine emotion recognition in speech, paving the way for more adaptive and context-aware systems in real-world applications.