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.
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References
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, 1986
Chou, W.: “Discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition”. Proceedings of the IEEE 88, 1201–1222 (2000)
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)
Duda, R. O., Hart, P. E., Stork, D.G.: Pattern Classification. Second Edition. Wiley, New York (2001)
Forney, G.D.: “The Viterbi algorithm”. Proceeding of IEEE 61, 268–278 (1973)
Fukunaga, K.: Introduction to statistical pattern recognition. Second edition. Academic Press Inc., New York (1990)
Juang, B.-H.: “Maximum-likelihood estimation for mixture multivariate stochastic observations of Markov chains”. AT&T Technical Journal 64, 1235–1249 (1985)
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)
Juang, B.-H., Katagiri, S.: “Discriminative learning for minimum error classification”. IEEE Transactions on Signal Processing 40, 3043–3054 (1992)
Korkmazskiy F., Juang B.-H.: “Discriminative adaptation for speaker verification.” in Proceedings of Int. Conf. on Spoken Language Processing (Philadelphia), pp. 28–31, 1996
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)
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)
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)
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)
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)
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)
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 1998
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 1998
Viterbi, A. J.: “Error bounds for convolutional codes and an asymptotically optimal decoding algorithm”. IEEE Transactions on Information Theory IT-13, 260–269 (1967)
Wald, A.: Sequential analysis. Second edition. Chapman & Hall, NY (1947)
Wu, C. F.J.: “On the convergence properties of the EM algorithm”. The Annals of Statistics 11, 95–103 (1983)
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Li, Q.(. (2012). Non-Stationary Pattern Recognition. In: Speaker Authentication. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23731-7_4
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DOI: https://doi.org/10.1007/978-3-642-23731-7_4
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