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This paper presents an innovative approach to address the challenges oftranslating multi-modal emotion recognition models to a more practical andresource-efficient uni-modal counterpart, specifically focusing on speech-onlyemotion recognition. Recognizing emotions from speech signals is a criticaltask with applications in human-computer interaction, affective computing, andmental health assessment. However, existing state-of-the-art models often relyon multi-modal inputs, incorporating information from multiple sources such asfacial expressions and gestures, which may not be readily available or feasiblein real-world scenarios. To tackle this issue, we propose a novel frameworkthat leverages knowledge distillation and masked training techniques.

Bridging Modalities: Knowledge Distillation and Masked Training for  Translating Multi-Modal Emotion Recognition to Uni-Modal, Speech-Only Emotion  Recognition