Abstract
Motivated by applications to automatic machine recognition of speech, nongaussian parametric density estimation for classification of acoustic feature vectors, which are known to have very high dimensionality, is studied in a maximum likelihood framework. We use EM type algorithms for the estimation of parameters for a mixture model of nongaussian densities. Our experience with these techniques in the context of large vocabulary continuous speech recognition is reported. Experimental results tend to indicate that nongaussian mixture component densities model speech data more effectively from this point of view. Comments are made on the convergence of the iterative estimation algorithms developed.
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Basu, S., Micchelli, C.A. (1998). Parametric Density Estimation for the Classification of Acoustic Feature Vectors in Speech Recognition. In: Suykens, J.A.K., Vandewalle, J. (eds) Nonlinear Modeling. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5703-6_4
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DOI: https://doi.org/10.1007/978-1-4615-5703-6_4
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