Emotion Encoder

Emotion encoders are machine learning models designed to extract and represent emotional information from various modalities, such as speech, text, and facial expressions. Current research focuses on improving the accuracy and robustness of these encoders, often employing multimodal architectures that integrate information from multiple sources and leveraging techniques like disentanglement and instruction tuning to enhance performance. This work is significant for advancing multimodal emotion recognition and reasoning, with applications ranging from improving human-computer interaction and mental health support to detecting synthetic text and hate speech online.