Spoofing detection is today a mainstream research topic. Standard metrics can be applied to evaluate the performance of isolated spoofing detection solutions and others have been proposed to support their evaluation when they are combined with speaker detection. These either have well-known deficiencies or restrict the architectural approach to combine speaker and spoof detectors. In this paper, we propose an architecture-agnostic detection cost function (a-DCF). A generalisation of the original DCF used widely for the assessment of automatic speaker verification (ASV), the a-DCF is designed for the evaluation of spoofing-robust ASV. Like the DCF, the a-DCF reflects the cost of decisions in a Bayes risk sense, with explicitly defined class priors and detection cost model. We demonstrate the merit of the a-DCF through the benchmarking evaluation of architecturally-heterogeneous spoofing-robust ASV solutions.

翻译：欺诈检测如今已成为主流研究课题。标准度量可应用于评估独立欺诈检测方案的性能，而其他度量则被提出用于支持其在结合说话人检测时的评估。这些度量要么存在众所周知的缺陷，要么限制了结合说话人与欺诈检测器的架构方法。本文提出一种架构无关的检测代价函数（a-DCF）。作为广泛用于自动说话人验证（ASV）评估的原始DCF的泛化，a-DCF专为评估抗欺诈鲁棒ASV而设计。与DCF类似，a-DCF在贝叶斯风险意义上反映决策代价，并明确定义类别先验与检测代价模型。我们通过对架构异构的鲁棒抗欺诈ASV方案进行基准测试评估，证明了a-DCF的优越性。