{"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/the-eurecom-submission-to-the-first-dihard","title":"The EURECOM Submission to the First DIHARD Challenge","arxiv_id":null,"date":"2018-09-06","proceeding":null,"authors":["Jose Patino","Héctor Delgado","Nicholas Evans"],"abstract":"The first DIHARD challenge aims to promote speaker diarization research and to foster progress in domain robustness. This paper reports EURECOM's submission to the DIHARD challenge. It is based upon a low-resource, domain-robust binary key approach to speaker modelling. New contributions include the use of an infinite impulse response - constant Q Mel-frequency cepstral coefficient (ICMC) front-end, a clustering selection / stopping criterion algorithm based on spectral clustering and a mechanism to detect single-speaker trials. Experimental results obtained using the standard DIHARD database show that the contributions reported in this paper deliver relative improvements of 39% in terms of the diarization error rate over the baseline algorithm. An absolute DER of 29% on the evaluation set compares favourably with those of competing systems, especially given that the binary key system is highly efficient, running 63 times faster than real-time.","url_abs":"https://isca-speech.org/archive/Interspeech_2018/abstracts/2172.html","url_pdf":"https://isca-speech.org/archive/Interspeech_2018/pdfs/2172.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":"the-eurecom-submission-to-the-first-dihard","repo_url":"https://github.com/josepatino/pyBK","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"speaker-diarization","task_name":"Speaker Diarization"},{"task_slug":"speaker-diarization","task_name":"speaker-diarization"}],"methods":[{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}