a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要