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

• URL: http://arxiv.org/abs/2401.03000v1
• Date: Thu, 4 Jan 2024 22:42:14 GMT
• Title: Bridging Modalities: Knowledge Distillation and Masked Training for Translating Multi-Modal Emotion Recognition to Uni-Modal, Speech-Only Emotion Recognition
• Authors: Muhammad Muaz and Nathan Paull and Jahnavi Malagavalli
• Abstract summary: This paper presents an innovative approach to address the challenges of translating multi-modal emotion recognition models to a more practical uni-modal counterpart. Recognizing emotions from speech signals is a critical task with applications in human-computer interaction, affective computing, and mental health assessment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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