{"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/but-system-for-the-second-dihard-speech","title":"BUT System for the Second DIHARD Speech Diarization Challenge","arxiv_id":"2002.11356","date":"2020-02-26","proceeding":null,"authors":[],"abstract":"This paper describes the winning systems developed by the BUT team for the\nfour tracks of the Second DIHARD Speech Diarization Challenge. For tracks 1 and\n2 the systems were mainly based on performing agglomerative hierarchical\nclustering (AHC) of x-vectors, followed by another x-vector clustering based on\nBayes hidden Markov model and variational Bayes inference. We provide a\ncomparison of the improvement given by each step and share the implementation\nof the core of the system. For tracks 3 and 4 with recordings from the Fifth\nCHiME Challenge, we explored different approaches for doing multi-channel\ndiarization and our best performance was obtained when applying AHC on the\nfusion of per channel probabilistic linear discriminant analysis scores.","url_abs":"http://arxiv.org/abs/2002.11356v1","url_pdf":"http://arxiv.org/pdf/2002.11356v1.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":"but-system-for-the-second-dihard-speech","repo_url":"https://github.com/MagicHub-io/Magic-Data-ASR-SD-Challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}