Multi-layer perceptrons and probabilistic neural networks for phoneme recognition
نویسندگان
چکیده
Phoneme recognition can be viewed as classifying multivariate observations. Multi-layer perceptrons (MLP) and probabilistic neural networks (PNN) approach the decision problem using two complementary models. The MLP models the discriminant surfaces between different phoneme categories, essentially by piece-wise planar approximations, while the PNN approximates class conditional probability densities by a Gaussian mixture and has no explicit model of the discriminants. In the two networks, connection weights correspond to normal vectors and mean values respectively.
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