SVM Applied to the Generation of Biometric Speech Key

  • L. Paola García-Perera
  • Carlos Mex-Perera
  • Juan A. Nolazco-Flores
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


In this research we present a new scheme for the generation of a biometric key based on Automatic Speech Technology and Support Vector Machines. Keys are produced by making a distinction among the voice attributes of the users employing hyperplanes. It is described how the key is conformed and the reliability of the method. We depict an experimental evaluation for different values of the parameters. Among the different kernels for the Support Vector Machine, the RBF obtained the best results.


Support Vector Machine Radial Basis Function Support Vector Machine Model Automatic Speech Recognition Radial Basis Function Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory (1992)Google Scholar
  2. 2.
    Jr. Campbell, J.P.: Features and Measures for Speaker Recognition. Ph.D. Dissertation, Oklahoma State University (1992)Google Scholar
  3. 3.
    Cortes, C., Vapnik, V.: Support-vector network. Machine Learning 20, 273–297 (1995)zbMATHGoogle Scholar
  4. 4.
    Higgins, A., Porter, J., Bahler, L.: YOHO Speaker Authentication Final Report. ITT Defense Communications Division (1989)Google Scholar
  5. 5.
    HTK Hidden Markov Model Toolkit home page,
  6. 6.
    Monrose, F., Reiter, M.K., Li, Q., Wetzel, S.: Cryptographic Key Generation From Voice. In: Proceedings of the IEEE Conference on Security and Privacy, Oakland, CA (May 2001)Google Scholar
  7. 7.
    Morris, R., Thompson, K.: Password security: A case history. Communications of the ACM 22(11), 594–597 (1979)CrossRefGoogle Scholar
  8. 8.
    Müller, K.-R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Neural Networks 12(2), 181–201 (2001)CrossRefGoogle Scholar
  9. 9.
    Osuna, E., Freund, R., Girosi, F.: Support vector machines: Training and applications. Technical Report AIM-1602, MIT A.I. Lab. (1996)Google Scholar
  10. 10.
    Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)Google Scholar
  11. 11.
    Rabiner, L.R., Juang, B.-H.: Fundamentals of speech recognition. Prentice-Hall, New-Jersey (1993)Google Scholar
  12. 12.
    Joachims, T.: SVMLight: Support Vector Machine, SVM-Light Support Vector Machine, University of Dortmund (November 1999),

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • L. Paola García-Perera
    • 1
  • Carlos Mex-Perera
    • 1
  • Juan A. Nolazco-Flores
    • 1
  1. 1.Computer Science DepartmentITESM, Campus MonterreyMonterreyMéxico

Personalised recommendations