Emotion Embeddings

Emotion embeddings represent emotional content from various modalities (text, audio, video) as numerical vectors, aiming to enable machines to understand and respond to human emotions more effectively. Current research focuses on integrating these embeddings into various architectures, including large language models, diffusion models, and deep metric learning approaches, to improve tasks like empathetic response generation, emotion-aware speech synthesis, and mental health diagnosis. This work is significant for advancing affective computing, with applications ranging from more human-like chatbots and improved text-to-speech systems to more accurate and efficient mental health assessments.