Abstract
Current speaker authentication algorithms are largely based on multivariate statistical theory. In this chapter, we introduce the most important technical components and concepts of multivariate analysis as they apply to speaker authentication: the multivariate Gaussian (also called normal) distribution, principal component analysis (PCA), vector quantization (VQ), and segmental K-means. These fundamental techniques have been used for statistical pattern recognition and will be used in our further discussions throughout this book. Understanding the basic concepts of these techniques is essential for understanding and developing speaker authentication algorithms.
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References
Duda, R. O., Hart, P. E., Stork, D. G.: Pattern Classification, Second edn. John & Wiley, New York (2001)
Fischer, T. R., Dicharry, R. M.: “Vector quantizer design for gaussian, gamma, and laplacian sources”. IEEE Transactions on Communications. COM-32, 1065–1069 (1984)
Gray, R. M., Linde, Y.: “Vector quantizers and predictive quantizers for Gauss-Markov sources”. IEEE Transactions on Communications. COM-30, 381–389 (1982)
Huang, C. M., Harris, R. W.: “A comparison of several vector quantization codebook generation approaches”. IEEE Transactions on Image Processing 2, 108–112 (1993)
Huang, X., Acero, A., Acero, A., Acero, A.: Spoken language processing. Prentice Hall PTR, NJ (2001)
Johnson, R. A., Wichern, D. W.: Applied Multivariate Statistical Analysis. Prentice Hall, New Jersey (1988)
Juang, B.-H., Rabiner, L. R.: “The segmental k-means algorithm for estimating parameters of hidden Markove models”. IEEE Trans. Acoustics, Speech and Signal Processing 38, 1639–1641 (1990)
Li, Q., Swaszek, P. F., “One-pass vector quantizer design by sequential pruning of the training data,” in Proceedings of International Conference on Image Processing (Washington, DC), October 1995
Li, Q. and Tufts, D. W., “Improving discriminant neural network (DNN) design by the use of principal component analysis,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (Detroit, MI), pp. 3375–3379, May 1995
Li, Q. and Tufts, D. W., “Synthesizing neural networks by sequential addition of hidden nodes,” in Proceedings of the IEEE International Conference on Neural Networks (Orlando, FL), pp. 708–713, June 1994
Li, Q., Tufts, D. W., Duhaime, R., August, P., “Fast training algorithms for large data sets with application to classification of multispectral images,” in Proceedings of the IEEE 28th Asilomar Conference (Pacific Grove), October 1994
Linde, Y., Buzo, A., Gray, R. M.: “An algorithm for vector quantizer design”. IEEE Transactions on Communications. COM-28, 84–95 (1980)
MacQueen, J., “Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. Math. Stat., Prob. pp. 281–296 (1967)
Max, J.: “Quantizing for minimum distortion”. IEEE Transactions on Information Theory. IT-6, 7–12 (1960)
Paez, M. D., Glisson, T. H.: “Minimum mean-square-error quantization in speech pcm and dpcm systems”. IEEE Transactions on Communications. COM-20, 225–230 (1972)
Rabiner, L. R., Wilpon, J. G., Wilpon, J. G., Wilpon, J. G.: “A segmental k-means training procedure for connected word recognition”. AT&T Technical Journal 65, 21–31 (1986)
Soong, F. K., Rosenberg, A. E., Juang, B.-H.: “A vector quantization approach to speaker recognition”. AT&T Technical Journal 66, 14–26 (1987)
Swaszek, P. F., “Low dimension/moderate bitrate vector quantizers for the laplace source,” in Abstracts of IEEE International Symposium on Information Theory, p. 74, (1990)
Swaszek, P. F., “Vector quantization for image compression,” in Proceedings of Princeton Conference on Information Sciences and Systems (Princeton, NJ), pp. 254–259, March 1986
Swaszek, P. F., Narasimhan, A., “Quantization of the correlated gaussian source,” in Proceedings of Princeton Conference on Information Sciences and Systems (Princeton, NJ), pp. 784–789, March 1988
Swaszek, P. F., Thomas, J. B.: “Optimal circularly symmetric quantizers”. Journal of Franklin Institute 313, 373–384 (1982)
Tufts, D. W. and Li, Q., “Principal feature classification,” in Neural Networks for Signal Processing V, Proceedings of the 1995 IEEE Workshop (Cambridge, MA), August 1995
Viterbi, A. J.: “Error bounds for convolutional codes and an asymptotically optimal decoding algorithm”. IEEE Transactions on Information Theory. IT-13, 260–269 (1967)
Wilson, S. G.: “Magnitude/phase quantization of independent gaussian variates”. IEEE Transactions on Communications. COM-28, 1924–1929 (1980)
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Li, Q.(. (2012). Multivariate Statistical Analysis and One-Pass Vector Quantization. In: Speaker Authentication. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23731-7_2
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DOI: https://doi.org/10.1007/978-3-642-23731-7_2
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