The integration of hidden Markov models (HMMs) and neural networks is an important research line to obtain new speech recognition systems that combine a good time-alignment capability and a powerful discrimination-based training. The recurrent network automata (RNA) model is a hybrid of a recurrent neural network, which estimates the state emission probability of a HMM, and a dynamic programming, which finds the best state sequence. This paper reports the results obtained with the RNA model, after three years of research and application in speaker independent digit recognition over the public telephone network.
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