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. Index Terms — Classifier selection, linear fusion 1.

Fusion of the base classifiers is seen as the way to achieve stateof-the art performance in the speaker verfication systems. Standard approach is to pose the fusion problem as the linear binary classification task. Most successful loss function in speaker verification fusion has been the weighted logistic regression popularized by the FoCal toolkit. However, it is known that optimizing logistic regression can overfit severely without appropriate regularization. In addition, subset classifier selection can be achieved by using an external 0/1 loss function on the best subset. In this work, we propose to use LASSO based regularization on the FoCal cost function to achive improved performance and classifier subset selection method integrated into one optimization task. Proposed method is able to achieve 51 % relative improvement in Actual DCF over the FoCal baseline. Index Terms: logistic regression, regularization, compressed sensing, linear fusion, speaker verification