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