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a-DCF: an architecture agnostic metric with application to spoofing-robust speaker verification
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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.
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Cited by 2 Pith papers
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Large Audio Language Models for Spoofing-Aware Speaker Verification
Adapted LALMs can reach competitive spoofing-aware speaker verification (89.3% accuracy, 0.19 min a-DCF on an ASVspoof5 subset), though zero-shot performance is near chance.
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WildSpoof Challenge Evaluation Plan
WildSpoof is a two-track competition plan: generate spoofed speech with cloned voices (TTS) and detect spoofed speech in speaker verification (SASV), both on in-the-wild audio.
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