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Abstract:
Logistic units in the first hidden layer of a feedforward
neural network compute the relative probability of a data point
under two Gaussians. This leads us to consider substituting other
density models. We present an architecture for performing
discriminative learning of Hidden Markov Models using a network
of many small HMM's. Experiments on speech data show it to be
superior to the standard method of discriminatively training
HMM's.
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