Speech recognition using a sequential neural network
W. Luz, Yutaka Kobayashi, Yasuhisa Niimi · 1991
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 mod ba mod , mod da mod , and mod ga mod .>