  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.
