Emotion Class

Emotion classification research aims to automatically categorize emotional states from various modalities like speech, facial expressions, and physiological signals. Current efforts focus on developing robust and interpretable models, often employing deep learning architectures such as Convolutional Neural Networks (CNNs) and Transformers, along with granular neural networks and Bayesian approaches to handle label uncertainty and improve accuracy. This field is crucial for advancing human-computer interaction, mental health monitoring, and the development of more emotionally intelligent artificial intelligence systems.