{"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/filter-based-multi-task-cross-corpus-feature","title":"Filter-based multi-task cross-corpus feature learning for speech emotion recognition","arxiv_id":null,"date":"2024-02-20","proceeding":"Signal, Image and Video Processing 2024 2","authors":["Behzad Bakhtiari","Elham Kalhor","Seyed Hossein Ghafarian"],"abstract":"Speech emotion recognition is a highly active field of research in human–machine interaction. A primary challenge faced\r\nby researchers in this area is how to tackle the problem of changing data distribution. In the last decade, studies have\r\nproposed excellent methods to address this issue, one of which is multi-task learning. Previous works employing multi-task\r\nlearning for speech emotion recognition have been wrapper-based, meaning that both feature selection and classification\r\nare simultaneously solved. The current study utilizes one of the classic multi-task learning algorithms to introduce a simple\r\nyet effective multi-task learning approach for speech emotion recognition. In investigating the effectiveness of its proposed\r\nmethod, the present research experiments on eight well-known public speech emotion corpora and compares the results with\r\neight of the best approaches in the literature. Encouraged by the results in both simplicity and efficiency, the current authors go\r\non to conduct a more intensive exploration of their proposed method. This process offers a set of features for speech emotion\r\nrecognition as a cross-corpus feature set. In the present study, the proposed feature set is tested with corpus and cross-corpus\r\nscenarios with seven corpora. Furthermore, the current work applies the proposed feature set on a new corpus with a language\r\nnot present in earlier data. Extensive experiments show superior results.","url_abs":"https://link.springer.com/article/10.1007/s11760-023-02977-2","url_pdf":"https://static-content.springer.com/esm/art%3A10.1007%2Fs11760-023-02977-2/MediaObjects/11760_2023_2977_MOESM1_ESM.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":"filter-based-multi-task-cross-corpus-feature","repo_url":"https://github.com/BakhtiariB/MTCCFLset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cross-corpus","task_name":"Cross-corpus"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"speech-emotion-recognition","task_name":"Speech Emotion Recognition"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}