  Score calibration enables automatic speaker recognizers to make
cost-effective accept / reject decisions. Traditional calibration requires
supervised data, which is an expensive resource. We propose a 2-component GMM
for unsupervised calibration and demonstrate good performance relative to a
supervised baseline on NIST SRE'10 and SRE'12. A Bayesian analysis demonstrates
that the uncertainty associated with the unsupervised calibration parameter
estimates is surprisingly small.
