Bridging Modalities: Knowledge Distillation and Masked Training for Translating Multi-Modal Emotion Recognition to Uni-Modal, Speech-Only Emotion Recognition
This addresses the challenge of deploying emotion recognition in real-world scenarios where multi-modal data is unavailable, though it appears incremental as it builds on existing knowledge distillation and training methods.
The paper tackles the problem of translating multi-modal emotion recognition models to a more practical speech-only version, proposing a framework that uses knowledge distillation and masked training techniques to achieve this translation.
This paper presents an innovative approach to address the challenges of translating multi-modal emotion recognition models to a more practical and resource-efficient uni-modal counterpart, specifically focusing on speech-only emotion recognition. Recognizing emotions from speech signals is a critical task with applications in human-computer interaction, affective computing, and mental health assessment. However, existing state-of-the-art models often rely on multi-modal inputs, incorporating information from multiple sources such as facial expressions and gestures, which may not be readily available or feasible in real-world scenarios. To tackle this issue, we propose a novel framework that leverages knowledge distillation and masked training techniques.