12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Regularized Logistic Regression Fusion for Speaker Verification

Ville Hautamäki (1), Kong Aik Lee (1), Tomi Kinnunen (2), Bin Ma (1), Haizhou Li (1)

(1) A*STAR, Singapore
(2) University of Eastern Finland, Finland

Fusion of the base classifiers is seen as the way to achieve state-ofthe art performance in the speaker verification 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 achieve 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.

Full Paper

Bibliographic reference.  Hautamäki, Ville / Lee, Kong Aik / Kinnunen, Tomi / Ma, Bin / Li, Haizhou (2011): "Regularized logistic regression fusion for speaker verification", In INTERSPEECH-2011, 2745-2748.