Multi Label Emotion

Multi-label emotion recognition focuses on identifying multiple emotions simultaneously within a single data instance (e.g., speech, text, or video), moving beyond the limitations of single-emotion classification. Current research emphasizes improving model performance through advanced fusion techniques for multimodal data (combining audio, visual, and textual information), data augmentation strategies to address data scarcity, and the incorporation of label correlations and contextual information. This field is significant for advancing human-computer interaction, improving mental health assessments from patient narratives, and enhancing applications like emotional speech synthesis and personalized healthcare.