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Extrapolating false alarm rates in automatic speaker verification

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Document pages: 5 pages

Abstract: Automatic speaker verification (ASV) vendors and corpus providers would bothbenefit from tools to reliably extrapolate performance metrics for largespeaker populations without collecting new speakers. We address false alarmrate extrapolation under a worst-case model whereby an adversary identifies theclosest impostor for a given target speaker from a large population. Our modelsare generative and allow sampling new speakers. The models are formulated inthe ASV detection score space to facilitate analysis of arbitrary ASV systems.

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