Chapter

Multiple Classifier Systems

Volume 3541 of the series Lecture Notes in Computer Science pp 356-365

Speaker Verification Using Adapted User-Dependent Multilevel Fusion

  • Julian Fierrez-AguilarAffiliated withCarnegie Mellon UniversityBiometrics Research Lab./ATVS, Escuela Politecnica Superior, Universidad Autonoma de Madrid
  • , Daniel Garcia-RomeroAffiliated withCarnegie Mellon UniversityBiometrics Research Lab./ATVS, Escuela Politecnica Superior, Universidad Autonoma de Madrid
  • , Javier Ortega-GarciaAffiliated withCarnegie Mellon UniversityBiometrics Research Lab./ATVS, Escuela Politecnica Superior, Universidad Autonoma de Madrid
  • , Joaquin Gonzalez-RodriguezAffiliated withCarnegie Mellon UniversityBiometrics Research Lab./ATVS, Escuela Politecnica Superior, Universidad Autonoma de Madrid

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Abstract

In this paper we study the application of user-dependent score fusion to multilevel speaker recognition. After reviewing related works in multimodal biometric authentication, a new score fusion technique is described. The method is based on a form of Bayesian adaptation to derive the personalized fusion functions from prior user-independent data. Experimental results are reported using the MIT Lincoln Laboratory’s multilevel speaker verification system. It is experimentally shown that the proposed adapted fusion method outperforms both user independent and non-adapted user-dependent fusion approaches.