Word recognition with recurrent network automata

The authors report a method to directly encode temporal information into a neural network by explicitly modeling that information with a left-to-right automaton, and teaching a recurrent network to identify the automaton states. The state length and position are adjusted with the usual train and re-segment iterative procedure. The global model is a hybrid of a recurrent neural network which implements the state transition models, and dynamic programming, which finds the best state sequence. The advantages achieved by using recurrent networks are outlined by applying the method to a speaker-independent digit recognition task.<>