Speech recognition using a sequential neural network

The authors propose a hybrid model of the neural network and the DTW (dynamic time warping) algorithm. The model is basically a state transition system. Each state of the model has a neural network which is activated for some portion of a sequential speech pattern, for example, the first consonantal part of a monosyllable. States of the model are partially ordered corresponding to classes of sequential patterns. Based on the DTW algorithm, the activation values of the ordered states of a pattern class are summed up to evaluate the likelihood that an input pattern belongs to the class. This model has been applied to the discrimination among monosyllables like |ba|, |da|, and |ga|