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
Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Machine Learning (cs.LG)
2024-03-03 00:00:00
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.
PDF: a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification.pdf
Empowered by ChatGPT