Automatic Regularization Of Cross-Entropy Cost For Speaker Recognition Fusion

In this paper we study automatic regularization techniques for the fusion of automatic speaker recognition systems. Parameter regularization could dramatically reduce the fusion training time. In addition, there will not be any need for splitting the development set into different folds for cross-validation. We utilize majorization-minimization approach to automatic ridge regression learning and design a similar way to learn LASSO regularization parameter automatically. By experiments we show improvement in using automatic regularization.