MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions

• URL: http://arxiv.org/abs/2506.09556v1
• Date: Wed, 11 Jun 2025 09:41:23 GMT
• Title: MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions
• Authors: Georgios Chatzichristodoulou, Despoina Kosmopoulou, Antonios Kritikos, Anastasia Poulopoulou, Efthymios Georgiou, Athanasios Katsamanis, Vassilis Katsouros, Alexandros Potamianos,
• Abstract summary: MEDUSA is a multimodal framework with a four-stage training pipeline.<n>DeepSER is a novel extension of a deep cross-modal transformer fusion mechanism.<n>Manor MixUp is employed for further regularization.
• Score: 46.34220791244788
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: SER is a challenging task due to the subjective nature of human emotions and their uneven representation under naturalistic conditions. We propose MEDUSA, a multimodal framework with a four-stage training pipeline, which effectively handles class imbalance and emotion ambiguity. The first two stages train an ensemble of classifiers that utilize DeepSER, a novel extension of a deep cross-modal transformer fusion mechanism from pretrained self-supervised acoustic and linguistic representations. Manifold MixUp is employed for further regularization. The last two stages optimize a trainable meta-classifier that combines the ensemble predictions. Our training approach incorporates human annotation scores as soft targets, coupled with balanced data sampling and multitask learning. MEDUSA ranked 1st in Task 1: Categorical Emotion Recognition in the Interspeech 2025: Speech Emotion Recognition in Naturalistic Conditions Challenge.

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