Abstract
Gaussian mixture model (GMM) [1] has been widely used for modeling speakers. In speaker identification, one major problem is how to generate a set of GMMs for identification purposes based upon the training data. Due to the hill-climbing characteristic of the maximum likelihood (ML) method, any arbitrary estimate of the initial model parameters will usually lead to a sub-optimal model in practice. To resolve this problem, this paper proposes a hybrid training method based on the genetic algorithm (GA). It utilizes the global searching capability of the GA and combines the effectiveness of the ML method.
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References
Reynolds, D.A.: Speaker identification and verification using Gaussian mixture speaker models. Speech Communication 17, 91–108 (1995)
Kwong, S., Chau, C.W., Man, K.F., Tang, K.S.: Optimisation of HMM topology and its model parameters by genetic algorithms. Pattern Recognition 34, 509–522 (2001)
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© 2004 Springer-Verlag Berlin Heidelberg
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Hong, Q.Y., Kwong, S., Wang, H.L. (2004). Optimization of Gaussian Mixture Model Parameters for Speaker Identification. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_141
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DOI: https://doi.org/10.1007/978-3-540-24855-2_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
Online ISBN: 978-3-540-24855-2
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