Automatic Regularization of Cross-entropy Cost for Speaker Recognition Fusion

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.
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Communication dans un congrès
Annual Conference of the International Speech Communication Association (Interspeech), Aug 2013, Lyon, France
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https://hal.archives-ouvertes.fr/hal-01927590
Contributeur : Anthony Larcher <>
Soumis le : lundi 19 novembre 2018 - 23:59:13
Dernière modification le : jeudi 14 mars 2019 - 11:46:06
Document(s) archivé(s) le : mercredi 20 février 2019 - 16:30:28

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  • HAL Id : hal-01927590, version 1

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Ville Hautamäki, Kong Lee, David Van Leeuwen, Rahim Saeidi, Anthony Larcher, et al.. Automatic Regularization of Cross-entropy Cost for Speaker Recognition Fusion. Annual Conference of the International Speech Communication Association (Interspeech), Aug 2013, Lyon, France. 〈hal-01927590〉

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