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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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https://hal.archives-ouvertes.fr/hal-01927590
Contributor : Anthony Larcher <>
Submitted on : Monday, November 19, 2018 - 11:59:13 PM
Last modification on : Thursday, December 19, 2019 - 1:50:04 PM
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Ville Hautamäki, Kong Aik 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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