Support Vector Classifier with Linguistic Interpretation of the Kernel Matrix in Speaker Verification

  • Mariusz Bąk
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 59)


The paper shows that support vector classifier with linguistic interpretation of the kernel matrix can be effectively used in speaker verification. The kernel matrix is obtained by means of fuzzy clustering, based on global learning of fuzzy system with logical interpretation of if-then rules and with parametric conclusions. The kernel matrix is data-dependent and may be interpreted in terms of linguistic values related to the premises of if-then rules. Simulation results obtained for SPIDRE corpus are presented for comparison with traditional methods used in speaker verification.


support vector machine kernel matrix fuzzy system speaker verification 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boser, B.E., Guyon, I.M., Vapnik, V.: A training algorithm for optimal margin classifier. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, US, pp. 144–152 (1992)Google Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001),
  3. 3.
    Czogała, E., Łęski, J.: Fuzzy and Neuro-fuzzy Intelligent Systems. Physica-Verlag, Heidelberg (2000)zbMATHGoogle Scholar
  4. 4.
    Fan, R.E., Chen, P.H., Lin, C.J.: Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889–1918 (2005)MathSciNetGoogle Scholar
  5. 5.
    Łęski, J.: On support vector regression machines with linguistic interpretation of the kernal matrix. Fuzzy Sets and Systems 157, 1092–1113 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Martin, A., Doddington, F., Kamm, T., Ordowski, M., Przybocki, M.: The DET curve in assesment of detection task performance. In: Proceedings of the 5th European Conference on Speech Communication and Technology, pp. 1895–1898 (1997)Google Scholar
  7. 7.
    Rabiner, L.R., Schafer, R.W.: Digital processing of speech signals. Prentice Hall, Englewood Cliffs (1978)Google Scholar
  8. 8.
    Reynolds, D.A.: Speaker identification and verification using gaussian mixture speaker models. Speech Communication 17(1-2), 91–108 (1995)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mariusz Bąk
    • 1
  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland

Personalised recommendations