Extrapolating False Alarm Rates in Automatic Speaker Verification

Alexey Sholokhov, Tomi Kinnunen, Ville Vestman, Kong Aik Lee


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.


 DOI: 10.21437/Interspeech.2020-1090

Cite as: Sholokhov, A., Kinnunen, T., Vestman, V., Lee, K.A. (2020) Extrapolating False Alarm Rates in Automatic Speaker Verification. Proc. Interspeech 2020, 4218-4222, DOI: 10.21437/Interspeech.2020-1090.


@inproceedings{Sholokhov2020,
  author={Alexey Sholokhov and Tomi Kinnunen and Ville Vestman and Kong Aik Lee},
  title={{Extrapolating False Alarm Rates in Automatic Speaker Verification}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={4218--4222},
  doi={10.21437/Interspeech.2020-1090},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1090}
}