MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2407.05746v1
• Date: Mon, 8 Jul 2024 08:52:06 GMT
• Title: MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition
• Authors: Jarod Duret, Mickael Rouvier, Yannick Estève,
• Abstract summary: We submit to the 2024 edition of the MSP-Podcast Speech Emotion Recognition (SER) Challenge. This challenge is divided into two distinct tasks: Categorical Emotion Recognition and Emotional Attribute Prediction. Our approach employs an ensemble of models, each trained independently and then fused at the score level using a Support Vector Machine (SVM) This joint training methodology aims to enhance the system's ability to accurately classify emotional states.
• Score: 12.808666808009926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we detail our submission to the 2024 edition of the MSP-Podcast Speech Emotion Recognition (SER) Challenge. This challenge is divided into two distinct tasks: Categorical Emotion Recognition and Emotional Attribute Prediction. We concentrated our efforts on Task 1, which involves the categorical classification of eight emotional states using data from the MSP-Podcast dataset. Our approach employs an ensemble of models, each trained independently and then fused at the score level using a Support Vector Machine (SVM) classifier. The models were trained using various strategies, including Self-Supervised Learning (SSL) fine-tuning across different modalities: speech alone, text alone, and a combined speech and text approach. This joint training methodology aims to enhance the system's ability to accurately classify emotional states. This joint training methodology aims to enhance the system's ability to accurately classify emotional states. Thus, the system obtained F1-macro of 0.35\% on development set.

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