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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)

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.

Keywords

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

References

  1. 1.
    Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals. Prentice-Hall, Englewood Cliffs (1978)Google Scholar
  2. 2.
    Quatieri, T.F.: Discrete-Time Speech Signal Processing - Principles and Practice. Prentice-Hall (2002)Google Scholar
  3. 3.
    Reynolds, D.A.: An overview of automatic speaker recognition technology. In: Proc. Int. Conf. Acoust., Speech, and Signal Process. (ICASSP), pp. 4072–4075 (2002)Google Scholar
  4. 4.
    Chen, S.H., Wang, H.C.: Improvement of speaker recognition by combining residual and prosodic features with acoustic features. In: Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp. 93–96 (2004)Google Scholar
  5. 5.
    Yegnanarayana, B., Reddy, K.S., Kishore, S.P.: Source and system features for speaker recognition using AANN models. In: Proc. Int. Conf. Acoust.,Speech, Signal Process. (ICASSP), pp. 409–413 (2001)Google Scholar
  6. 6.
    Mary, L., Sri Rama Murty, K., Mahadeva Prasanna, S.R., Yegnanarayana, B.: Features for speaker and language identification. In: Proc. of the ISCA Tutorial and Research Workshop on Speaker and Language Recognition (Odyssey 2004), pp. 323–328 (2004)Google Scholar
  7. 7.
    Dong, X., Zhaohui, W.: Speaker Recognition using Continuous Density Support Vector Machines. Electronics Letters 37(17), 1099–1101 (2001)CrossRefGoogle Scholar
  8. 8.
    Wan, V., Renals, S.: SVMSVM: Support Vector Machine Speaker Verification Methodology. In: Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Hong Kong, vol. 2, pp. 221–224 (2003)Google Scholar
  9. 9.
    Karam, Z.N., Campbell, W.M.: A Multi-Class MLLR Kernel for SVM Speaker Recognition. In: Proc. Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp. 4117–4120 (April 2008)Google Scholar
  10. 10.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker Verification Using Adapted Gaussian Mixture Models. Digital Signal Processing 10(1-3), 19–41 (2000)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Campbell, W.M., Sturim, D.E., Reynolds, D.A.: Support Vector Machines using GMM supervectors for Speaker Verification. IEEE Signal Process. Lett. 13(5), 308–311 (2006)CrossRefGoogle Scholar
  13. 13.
    Amrouche, A., Debyeche, M., Taleb Ahmed, A., Rouvaen, J.M., Ygoub, M.C.E.: Efficient System for Speech Recognition in Adverse Conditions Using Nonparametric Regression. Engineering Applications on Artificial Intelligence 23(1), 85–94 (2010)CrossRefGoogle Scholar

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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