Automatic Regularization of Cross-entropy Cost for Speaker Recognition Fusion
Year: 2013
Type: article
Abstract: 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 reg-ularization parameter automatically. By experiments we show improvement in using automatic regularization.
Authors: Ville Hautamäki, Kong Aik Lee, David Van Leeuwen, Rahim Saeidi, Anthony Larcher, Tomi Kinnunen, Taufiq Hasan, Seyed Omid Sadjadi, Gang Liu, Hynek Boril, John H. L. Hansen, Benoit Fauve
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4321089269