The speaker partitioning problem.

We give a unification of several different speaker recognition problems in terms of the general speaker partitioning problem, where a set of N inputs has to be partitioned into subsets according to speaker. We show how to solve this problem in terms of a simple generative model and demonstrate performance on NIST SRE 2006 and 2008 data. Our solution yields probabilistic outputs, which we show how to evaluate with a cross-entropy criterion. Finally, we show improved accuracy of the generative model via a discriminatively trained re-calibration transformation of log-likelihoods.