{"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/speaker-diarization-with-region-proposal","title":"Speaker Diarization with Region Proposal Network","arxiv_id":"2002.06220","date":"2020-02-14","proceeding":null,"authors":["Zili Huang","Shinji Watanabe","Yusuke Fujita","Paola Garcia","Yiwen Shao","Daniel Povey","Sanjeev Khudanpur"],"abstract":"Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the \"who spoke when\" problem. Although the standard diarization systems can achieve satisfactory results in various scenarios, they are composed of several independently-optimized modules and cannot deal with the overlapped speech. In this paper, we propose a novel speaker diarization method: Region Proposal Network based Speaker Diarization (RPNSD). In this method, a neural network generates overlapped speech segment proposals, and compute their speaker embeddings at the same time. Compared with standard diarization systems, RPNSD has a shorter pipeline and can handle the overlapped speech. Experimental results on three diarization datasets reveal that RPNSD achieves remarkable improvements over the state-of-the-art x-vector baseline.","url_abs":"https://arxiv.org/abs/2002.06220v1","url_pdf":"https://arxiv.org/pdf/2002.06220v1.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":"speaker-diarization-with-region-proposal","repo_url":"https://github.com/HuangZiliAndy/RPNSD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"region-proposal","task_name":"Region Proposal"},{"task_slug":"speaker-diarization","task_name":"Speaker Diarization"},{"task_slug":"speaker-diarization","task_name":"speaker-diarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2002.06220","atlas_url":"https://app.syntology.ai/?focus=2002.06220","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}