  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.
