Abstract
Most of the state-of-the-art speech recognition systems use Hidden Markov Models as an acoustic model, since there is a powerful Expectation-Maximization algorithm for its training. One of the important components of the continuous HMM we focus on is an emission probability which can be approximated by the weighted sum of Gaussians. Although, EM is a very fast iterative algorithm it can only guarantee a convergence to a local result. Therefore, the initialization process determines the final result. We suggested here two modifications of genetic algorithms for the initialization of EM. They are compared to the results of the EM with the same number of local multi-starts.
Chapter PDF
Similar content being viewed by others
References
Peinado, A., Segura, J.C.: Speech Recognition over Digital Channels: Robustness and Standards. John Wiley and Sons Ltd, Chichester (2006)
Rabiner, L., Juang, B.-H.: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)
Rice, J.: Mathematical Statistics and Data Analysis, 2nd edn. Duxbury Press, Boston (1995)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39(1), 1–38 (1977)
McLachlan, G., Krishnan, T.: The EM Algorithm and Extensions. Wiley, New York (1997)
Redner, R., Walker, H.: Mixture densities, maximum likelihood and the EM algorithm. Society for Industrial and Applied Mathematics (SIAM) 26(2), 195–239 (1984)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Goldberg, D.E.: Real-coded genetic algorithms, virtual alphabets, and block. University of Illinois, Urbana (1990)
Nolle, L., Battersby, A., El-Mihoub, T.A., Hopgood, A.A.: Hybrid Genetic Algorithms: A Review (2006)
Davis, S.B., Mermelstein, P.: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech and Signal Processing 28(4) (1980)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zablotskiy, S., Pitakrat, T., Zablotskaya, K., Minker, W. (2011). GMM Parameter Estimation by Means of EM and Genetic Algorithms. In: Jacko, J.A. (eds) Human-Computer Interaction. Design and Development Approaches. HCI 2011. Lecture Notes in Computer Science, vol 6761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21602-2_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-21602-2_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21601-5
Online ISBN: 978-3-642-21602-2
eBook Packages: Computer ScienceComputer Science (R0)