Support Vector Machines and Joint Factor Analysis for Speaker Verification

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Abstract

This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the SVMs are applied on different sources of information produced by the JFA. These informations are the Gaussian Mixture Model supervectors and speakers and Common factors. We found that the use of JFA factors gave the best results especially when within class covariance normalization method is applied in the speaker factors space, in order to compensate for the channel effect. The new combination results are comparable to other classical JFA scoring techniques.


Bibtex (lrde.bib)

@InProceedings{	  dehak.09.icassp,
  author	= {Najim Dehak and Patrick Kenny and R\'eda Dehak and Ondrej
		  Glember and Pierre Dumouchel and Lukas Burget and
		  Valiantsina Hubeika and Fabio Castaldo},
  title		= {Support Vector Machines and Joint Factor Analysis for
		  Speaker Verification},
  booktitle	= {IEEE-ICASSP},
  year		= 2009,
  address	= {Taipei - Taiwan},
  month		= apr,
  abstract	= {This article presents several techniques to combine
		  between Support vector machines (SVM) and Joint Factor
		  Analysis (JFA) model for speaker verification. In this
		  combination, the SVMs are applied on different sources of
		  information produced by the JFA. These informations are the
		  Gaussian Mixture Model supervectors and speakers and Common
		  factors. We found that the use of JFA factors gave the best
		  results especially when within class covariance
		  normalization method is applied in the speaker factors
		  space, in order to compensate for the channel effect. The
		  new combination results are comparable to other classical
		  JFA scoring techniques.}
}