  We introduce a Bayesian solution for the problem in forensic speaker
recognition, where there may be very little background material for estimating
score calibration parameters. We work within the Bayesian paradigm of evidence
reporting and develop a principled probabilistic treatment of the problem,
which results in a Bayesian likelihood-ratio as the vehicle for reporting
weight of evidence. We show in contrast, that reporting a likelihood-ratio
distribution does not solve this problem. Our solution is experimentally
exercised on a simulated forensic scenario, using NIST SRE'12 scores, which
demonstrates a clear advantage for the proposed method compared to the
traditional plugin calibration recipe.
