{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/odyssey-2024-speech-emotion-recognition","title":"Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results","arxiv_id":null,"date":"2024-06-20","proceeding":"Odyssey: The Speaker and Language Recognition Workshop 2024 6","authors":["Lucas Goncalves","Ali N. Salman","Abinay R. Naini","Laureano Moro Velazquez","Thomas Thebaud","Leibny Paola Garcia","Najim Dehak","Berrak Sisman","Carlos Busso"],"abstract":"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","url_abs":"https://scholar.google.com/citations?view_op=view_citation&hl=en&user=M7AZbh8AAAAJ&citation_for_view=M7AZbh8AAAAJ:UebtZRa9Y70C","url_pdf":"https://ecs.utdallas.edu/research/researchlabs/msp-lab/publications/Goncalves_2024.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"odyssey-2024-speech-emotion-recognition","repo_url":"https://github.com/msplabresearch/MSP-Podcast_Challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"speech-emotion-recognition","task_name":"Speech Emotion Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-emotion-recognition-on-msp-podcast-1","task":"Speech Emotion Recognition","dataset":"MSP-Podcast (Activation)","model":"wavlm","rank_in_archive_order":2,"of":4,"metrics":{"CCC":"0.7465055"},"uses_additional_data":false},{"leaderboard":"/sota/speech-emotion-recognition-on-msp-podcast-2","task":"Speech Emotion Recognition","dataset":"MSP-Podcast (Dominance)","model":"wavlm","rank_in_archive_order":2,"of":4,"metrics":{"CCC":"0.6712493"},"uses_additional_data":false},{"leaderboard":"/sota/speech-emotion-recognition-on-msp-podcast","task":"Speech Emotion Recognition","dataset":"MSP-Podcast (Valence)","model":"wavlm","rank_in_archive_order":2,"of":4,"metrics":{"CCC":"0.6466753"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}