Classifier subset selection and fusion for speaker verification

State-of-the-art speaker verification systems consists of a number of complementary subsystems whose outputs are fused, to arrive at more accurate and reliable verification decision. In speaker verification, fusion is typically implemented as a linear combination of the subsystem scores. Parameters of the linear model are commonly estimated using the logistic regression method, as implemented in the popular FoCal toolkit. In this paper, we study simultaneous use of classifier selection and fusion. We study four alternative fusion strategies, three score warping techniques, and provide interesting experimental bounds on optimal classifier subset selection. Detailed experiments are carried out on the NIST 2008 and 2010 SRE corpora.