Papers › Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results
Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results
Lucas Goncalves, Ali N. Salman, Abinay R. Naini, Laureano Moro Velazquez, Thomas Thebaud, Leibny Paola Garcia, Najim Dehak, Berrak Sisman, Carlos Busso
The Odyssey 2024 Speech Emotion Recognition (SER) Challenge aims to enhance innovation in recognizing emotions from spontaneous speech, moving beyond traditional datasets derived from acted scenarios. It offers speaker-independent training, development, and an exclusive test set, all annotated for the two tracks explored in this challenge: categorical and attribute SER tasks. This initiative promotes collaboration among researchers to develop SER technologies that perform accurately in real-world settings, encouraging researchers to explore innovative approaches that leverage the latest advancements in audio processing for SER. In this paper, we provide a detailed description of the baseline, leaderboard, evaluation of the results, and a discussion of the key findings. The competition website with leaderboards, links to baseline code, and instructions can be found here: https://lab-msp.com/MSP-Podcast_Competition/leaderboard.php
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Emotion Recognition | MSP-Podcast (Activation) | wavlm | CCC | 0.7465055 | #2 of 4 | Archive leaderboard | report |
| Speech Emotion Recognition | MSP-Podcast (Dominance) | wavlm | CCC | 0.6712493 | #2 of 4 | Archive leaderboard | report |
| Speech Emotion Recognition | MSP-Podcast (Valence) | wavlm | CCC | 0.6466753 | #2 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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