Automatic Regularization of Cross-entropy Cost for Speaker Recognition Fusion - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

Automatic Regularization of Cross-entropy Cost for Speaker Recognition Fusion

Résumé

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.
Fichier principal
Vignette du fichier
I4UFusion_camera.pdf (87.99 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01927590 , version 1 (19-11-2018)

Identifiants

  • HAL Id : hal-01927590 , version 1

Citer

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⟩

Collections

UNIV-LEMANS LIUM
108 Consultations
38 Téléchargements

Partager

Gmail Facebook X LinkedIn More