Paper Info
Title
Extrapolating false alarm rates in automatic speaker verification
Abstract
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.
Year
DOI
Venue
2020
10.21437/Interspeech.2020-1090
INTERSPEECH
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Alexey Sholokhov100.34
Tomi Kinnunen2132386.67
Ville Vestman3296.42
Kong-Aik Lee470960.64