Emotion Transfer

Emotion transfer research focuses on transferring emotional information between different modalities (e.g., speech, facial expressions, EEG signals) and languages, aiming to improve emotion recognition and synthesis across diverse contexts. Current efforts leverage advanced deep learning models, including transformer networks, graph neural networks, and autoencoders, often incorporating contrastive learning and transfer learning techniques to enhance performance and efficiency. This field is significant for advancing affective computing, with applications ranging from improved human-computer interaction and cross-lingual speech synthesis to more accurate analysis of emotional states in various settings, including healthcare and mental health.