Emotion Distribution

Emotion distribution research focuses on modeling the inherent ambiguity and variability in emotional responses, moving beyond single-label classifications to represent emotions as probability distributions. Current work emphasizes methods like neural ordinary differential equations and Bayesian networks to capture temporal dynamics and individual differences in emotion perception, often incorporating techniques like data augmentation and uncertainty estimation to address data imbalance and annotation inconsistencies. This research is significant for improving the accuracy and robustness of emotion recognition systems across various modalities (speech, text, images), with applications in personalized recommendations, human-computer interaction, and understanding group dynamics.