Categorical Emotion

Categorical emotion recognition focuses on classifying emotional states into discrete categories (e.g., happiness, sadness, anger), a crucial task in fields like human-computer interaction and mental health. Current research emphasizes multi-modal approaches, combining speech and text data, and utilizes techniques like multi-task learning, contrastive learning, and self-supervised learning with various architectures including Siamese networks and attention mechanisms to improve accuracy and efficiency. These advancements aim to overcome challenges like data scarcity and the need for robust models capable of distinguishing between semantically similar emotions, ultimately leading to more accurate and reliable emotion recognition systems.