Emotion Estimation

Emotion estimation research aims to accurately predict and understand human emotional states using various data sources, including facial expressions, speech, physiological signals, and self-reported data. Current research focuses on developing robust multimodal deep learning models, such as transformers and convolutional neural networks, often incorporating attention mechanisms and generative techniques to address challenges like catastrophic forgetting and improve generalization across diverse datasets. This field is significant for its potential applications in improving human-computer interaction, mental health monitoring, and personalized healthcare, as well as informing the design of more empathetic and responsive technologies.