a-DCF: an architecture agnostic metric with application to
  spoofing-robust speaker verification
Hye-jin Shim Jee-weon Jung Tomi Kinnunen Nicholas Evans Jean-Francois Bonastre Itshak Lapidot
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.