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Automatic Regularization of Cross-entropy Cost for Speaker Recognition Fusion

Ville Hautamäki 1 Kong Aik Lee 2 David van Leeuwen 3 Rahim Saeidi 4 Anthony Larcher 5 Tomi Kinnunen 1 Taufiq Hasan Seyed Sadjadi Gang Liu Hynek Boril John Hansen Benoît Fauve 6
1 University of Eastern Finland
2 Institute for Infocomm Research - I²R [Singapore]
3 Centre for Language Studies
4 Speech and Image Processing Unit, Department of Computer Science and Statistics
5 LIUM - Laboratoire d'Informatique de l'Université du Mans
6 Swansea University
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
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Submitted on : Monday, November 19, 2018 - 11:59:13 PM
Last modification on : Thursday, December 19, 2019 - 1:50:04 PM
Long-term archiving on: : Wednesday, February 20, 2019 - 4:30:28 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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