CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way

However, existing CL approaches for emotion recognition often rely on heuristic, data-driven, or model-based definitions of sample difficulty, neglecting the difficulty for human perception, a critical factor in subjective tasks like emotion recognition. We propose CHUCKLE (Crowdsourced Human Understanding Curriculum for Knowledge Led Emotion Recognition), a perception-driven CL framework that leverages annotator agreement and alignment in crowd-sourced datasets to define sample difficulty, under the assumption that clips challenging for humans are similarly hard for machine learning models. Empirical results suggest that CHUCKLE increases the relative mean accuracy by 6.56% for LSTMs and 1.61% for Transformers over non-curriculum baselines, while reducing the number of gradient updates, thereby enhancing both training efficiency and model robustness. Index T erms-- emotion recognition, curriculum learning, intended label, human perception, computational efficiency, deep neural networks 1. INTRODUCTION Emotions shape human experience, influencing communication, decision-making, and social interaction. Automatic emotion recognition seeks to infer human affective states from multi-modal signals such as speech [1, 2, 3], text [3], facial expressions [3, 4], gestures [5, 6], and physiological signals [7, 8].