SVM based GMM supervector speaker recognition using LP residual signal

Authors:
Dalila Yessad;Abderrahmane Amrouche
Affiliations:
Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Bab Ezzouar, Algiers, Algeria;Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Bab Ezzouar, Algiers, Algeria
Venue:
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Year:
2012

Citing 2
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Abstract

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.