Non-Stationary Pattern Recognition

Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

So far, we have discussed pattern recognition for stationary signals. In this chapter, we will discuss pattern recognition for both stationary and nonstationary signals. In speaker authentication, some tasks, such as speaker identification, are treated as stationary pattern recognition while others, such as speaker verification, are treated as non-stationary pattern recognition. We will introduce the stochastic modeling approach for both stationary and nonstationary pattern recognition. We will also introduce the Gaussian mixture model (GMM) and the hidden Markov model (HMM), two popular models that will be used throughout the book.

Keywords

Hide Markov Model Speech Signal Gaussian Mixture Model Equal Error Rate Speaker Recognition 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bahl L. R., Brown P. F., de Souza P. V., Mercer R.L.: “Maximum mutual information estimation of hidden Markov model parameters for speech recognition.” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Tokyo), pp. 49–52, 1986Google Scholar
  2. 2.
    Chou, W.: “Discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition”. Proceedings of the IEEE 88, 1201–1222 (2000)CrossRefGoogle Scholar
  3. 3.
    Dempster, A. P., Laird, N.M., Rubin, D. B.: “Maximum likelihood from incomplete data via the EM algorithm”. Journal of Royal Statistical Society 39, 1–38 (1977)MathSciNetMATHGoogle Scholar
  4. 4.
    Duda, R. O., Hart, P. E., Stork, D.G.: Pattern Classification. Second Edition. Wiley, New York (2001)MATHGoogle Scholar
  5. 5.
    Forney, G.D.: “The Viterbi algorithm”. Proceeding of IEEE 61, 268–278 (1973)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fukunaga, K.: Introduction to statistical pattern recognition. Second edition. Academic Press Inc., New York (1990)MATHGoogle Scholar
  7. 7.
    Juang, B.-H.: “Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains”. AT&T Technical Journal 64, 1235–1249 (1985)MathSciNetMATHGoogle Scholar
  8. 8.
    Juang, B.-H., Chou, W., Lee, C.-H.: “Minimum classification error rate methods for speech recognition”. IEEE Trans. on Speech and Audio Process 5, 257–265 (1997)CrossRefGoogle Scholar
  9. 9.
    Juang, B.-H., Katagiri, S.: “Discriminative learning for minimum error classification”. IEEE Transactions on Signal Processing 40, 3043–3054 (1992)MATHCrossRefGoogle Scholar
  10. 10.
    Korkmazskiy F., Juang B.-H.: “Discriminative adaptation for speaker verification.” in Proceedings of Int. Conf. on Spoken Language Processing (Philadelphia), pp. 28–31, 1996Google Scholar
  11. 11.
    Li, Q.: “A detection approach to search-space reduction for HMM state alignment in speaker verification”. IEEE Trans. on Speech and Audio Processing 9, 569–578 (2001)CrossRefGoogle Scholar
  12. 12.
    Liu, C. S., Lee, C.-H., Chou, W., Juang, B.-H., Rosenberg, A. E.: “A study on minimum error discriminative training for speaker recognition”. Journal of the Acoustical Society of America 97, 637–648 (1995)CrossRefGoogle Scholar
  13. 13.
    Neyman, J., Pearson, E.S.: “On the problem of the most efficient tests of statistical hypotheses”. Phil. Trans. Roy. Soc. A 231, 289–337 (1933)CrossRefGoogle Scholar
  14. 14.
    Neyman J., Pearson E. S.: “On the use and interpretation of certain test criteria for purpose of statistical inference.” Biometrika,20A, pp. Pt I, 175–240; Pt II, (1928)Google Scholar
  15. 15.
    Normandin, Y., Cardin, R., Mori, R. D.: “High-performance connected digit recognition using maximum mutual information estimation”. IEEE Trans. on Speech and Audio Processing 2, 299–311 (1994)CrossRefGoogle Scholar
  16. 16.
    Rabiner, L. R., Wilpon, J. G., Juang, B.-H.: “A segmental k-means training procedure for connected word recognition”. AT&T Technical Journal 65, 21–31 (1986)Google Scholar
  17. 17.
    Rosenberg A. E., Siohan O., Parthasarathy S.: “Speaker verification using minimum verification error training.” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Seattle), pp. 105–108, May 1998Google Scholar
  18. 18.
    Siohan O., Rosenberg A. E., Parthasarathy S.: “Speaker identification using minimum verification error training.” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Seattle), pp. 109–112, May 1998Google Scholar
  19. 19.
    Viterbi, A. J.: “Error bounds for convolutional codes and an asymptotically optimal decoding algorithm”. IEEE Transactions on Information Theory IT-13, 260–269 (1967)CrossRefGoogle Scholar
  20. 20.
    Wald, A.: Sequential analysis. Second edition. Chapman & Hall, NY (1947)MATHGoogle Scholar
  21. 21.
    Wu, C. F.J.: “On the convergence properties of the EM algorithm”. The Annals of Statistics 11, 95–103 (1983)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg  2012

Authors and Affiliations

  1. 1.Li Creative Technologies (LcT), IncFlorham ParkUSA

Personalised recommendations