t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
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DOI：
10.21437/odyssey.2018-44
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发表时间：
2018-04
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通讯作者：
T. Kinnunen;Kong Aik LEE;H. Delgado;N. Evans;M. Todisco;Md. Sahidullah;J. Yamagishi;D. Reynolds
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作者：
T. Kinnunen;Kong Aik LEE;H. Delgado;N. Evans;M. Todisco;Md. Sahidullah;J. Yamagishi;D. Reynolds
ASVspoof挑战赛系列的诞生是为了引领自动说话人验证（ASV）的反欺骗研究。2015年和2017年的两个挑战版本涉及使用等错误率（EER）指标评估与ASV隔离的欺骗对策（CM）。虽然在当时是一种战略性的评估方法，但它有某些缺点。首先，当ASV和CM组合时，CM EER不一定是性能的可靠预测器。其次，EER操作点不适合用户认证应用，例如电话银行，其特征在于高目标用户先验但低欺骗攻击先验。我们的目标是从CM迁移到ASV为中心的评估与援助的一个新的串联检测成本函数（t-DCF）度量。它将ASV研究中使用的传统DCF扩展到涉及欺骗攻击的场景。t-DCF度量有6个参数：（i）两个系统的虚警和未命中成本，以及（ii）目标和欺骗试验的先验概率（具有隐含的第三个非目标先验）。这项研究旨在作为一个独立的，辅导式的介绍。我们使用t-DCF分析了2015年和2017年ASVspoof版本中表现最佳的CM提交，重点关注之前的欺骗攻击。而有很少的选择之间的对策系统较低的先验，系统排名与EER和t-DCF显示较高的先验差异。我们观察到一些排名变化。调查结果支持采用DCF为基础的指标到未来ASVspoof挑战的路线图，并可能为其他生物识别反欺骗评估。
The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certain shortcomings. First, the CM EER is not necessarily a reliable predictor of performance when ASV and CMs are combined. Second, the EER operating point is ill-suited to user authentication applications, e.g. telephone banking, characterised by a high target user prior but a low spoofing attack prior. We aim to migrate from CM- to ASV-centric assessment with the aid of a new tandem detection cost function (t-DCF) metric. It extends the conventional DCF used in ASV research to scenarios involving spoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss costs for both systems, and (ii) prior probabilities of target and spoof trials (with an implied third, nontarget prior). The study is intended to serve as a self-contained, tutorial-like presentation. We analyse with the t-DCF a selection of top-performing CM submissions to the 2015 and 2017 editions of ASVspoof, with a focus on the spoofing attack prior. Whereas there is little to choose between countermeasure systems for lower priors, system rankings derived with the EER and t-DCF show differences for higher priors. We observe some ranking changes. Findings support the adoption of the DCF-based metric into the roadmap for future ASVspoof challenges, and possibly for other biometric anti-spoofing evaluations.