SVM Based GMM Supervector Speaker Recognition Using LP Residual Signal

  • Dalila Yessad
  • Abderrahmane Amrouche
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


Feature extraction is an important step for speaker recognition systems. In this paper, we generated MFCC (Mel Frequency Cepstral Coefficients) and LPCC (Linear Predictive Cepstral Coefficients) from LP residual of speech signal, instead their calculation directly from speech samples. These features represent complementary vocal cord information’s. In this work, Universal Background Gaussian Mixture Models (GMM-UBM) and Gaussian Supervector (GMM-SVM ) based speaker modeling have been used. Experimental results, using, ARADIGITS data-base, show the efficiency of the GMM-SVM based approach associated with feature vectors issued from LP residual signal.


LPC LPCC LP residual MFCC GMM-UBM GMM-SVM Speaker Recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dalila Yessad
    • 1
  • Abderrahmane Amrouche
    • 1
  1. 1.Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer SciencesUSTHBBab EzzouarAlgeria

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