Abstract
An architecture for a neural network that implements a hidden Markov model (HMM) is presented. This HMM net suggests integrating signal preprocessing (such as vector quantization) with the classifier. A minimum mean-squared-error training criterion for the HMM/neural net is presented and compared to maximum-likelihood and maximum-mutual-information criteria. The HMM forward-backward algorithm is shown to be the same as the neural net backpropagation algorithm. The implications of probability constraints on the HMM parameters are discussed. Relaxing these constraints allows negative probabilities, equivalent to inhibitory connections. A probabilistic interpretation is given for a network with negative, and even complex-valued, parameters. >
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