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Speaker Verification Using Accumulative Vectors with Support Vector Machines

  • Manuel Aguado Martínez
  • Gabriel Hernández-Sierra
  • José Ramón Calvo de Lara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

The applications of Support Vector Machines (SVM) in speaker recognition are mainly related to Gaussian Mixtures and Universal Background Model based supervector paradigm. Recently, has been proposed a new approach that allows represent each acoustic frame in a binary discriminant space. Also a representation of a speaker - called accumulative vectors - obtained from the binary space has been proposed. In this article we show results obtained using SVM with the accumulative vectors and Nuisance Attribute Projection (NAP) as a method for compensating the session variability. We also introduce a new method to counteract the effects of the signal length in the conformation of the accumulative vectors to improve the performance of SVM.

Keywords

speaker recognition binary values accumulative vectors Support Vector Machine Nuisance Attribute Projection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Manuel Aguado Martínez
    • 1
  • Gabriel Hernández-Sierra
    • 1
  • José Ramón Calvo de Lara
    • 1
  1. 1.Advanced Technologies Application CenterHavanaCuba

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