{"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/t-dcf-a-detection-cost-function-for-the","title":"t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification","arxiv_id":"1804.09618","date":"2018-04-25","proceeding":null,"authors":["Tomi Kinnunen","Kong Aik Lee","Hector Delgado","Nicholas Evans","Massimiliano Todisco","Md Sahidullah","Junichi Yamagishi","Douglas A. Reynolds"],"abstract":"The ASVspoof challenge series was born to spearhead research in anti-spoofing\nfor automatic speaker verification (ASV). The two challenge editions in 2015\nand 2017 involved the assessment of spoofing countermeasures (CMs) in isolation\nfrom ASV using an equal error rate (EER) metric. While a strategic approach to\nassessment at the time, it has certain shortcomings. First, the CM EER is not\nnecessarily a reliable predictor of performance when ASV and CMs are combined.\nSecond, the EER operating point is ill-suited to user authentication\napplications, e.g. telephone banking, characterised by a high target user prior\nbut a low spoofing attack prior. We aim to migrate from CM- to ASV-centric\nassessment with the aid of a new tandem detection cost function (t-DCF) metric.\nIt extends the conventional DCF used in ASV research to scenarios involving\nspoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss\ncosts for both systems, and (ii) prior probabilities of target and spoof trials\n(with an implied third, nontarget prior). The study is intended to serve as a\nself-contained, tutorial-like presentation. We analyse with the t-DCF a\nselection of top-performing CM submissions to the 2015 and 2017 editions of\nASVspoof, with a focus on the spoofing attack prior. Whereas there is little to\nchoose between countermeasure systems for lower priors, system rankings derived\nwith the EER and t-DCF show differences for higher priors. We observe some\nranking changes. Findings support the adoption of the DCF-based metric into the\nroadmap for future ASVspoof challenges, and possibly for other biometric\nanti-spoofing evaluations.","url_abs":"http://arxiv.org/abs/1804.09618v2","url_pdf":"http://arxiv.org/pdf/1804.09618v2.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":"t-dcf-a-detection-cost-function-for-the","repo_url":"https://github.com/magnumresearchgroup/auxiliaryrawnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"speaker-verification","task_name":"Speaker Verification"}],"methods":[],"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}