A new connectionist architecture for word spotting
Michael A. Franzini · 1992
A connectionist architecture and training procedure is proposed for the purpose of recognizing continuous speech using a word spotting approach. Standard backpropagation networks require a great deal of hardware to recognize the most fundamental features of speech signals. The proposed network architecture consists of units, each of which has a target input vector which represents the feature that fully activates the unit. The output of the unit is inversely related to the Euclidean distance of the unit's actual input vector to its target input vector. The network is trained by gradient descent, using a procedure derived in the same manner as the standard backpropagation training procedure. Only preliminary tests have been run, using a single-speaker isolated-word database of spelled Spanish words, with a vocabulary consisting of the 29 letters of the Spanish alphabet. The recognition rate using the proposed architecture was 94.0%, compared with 92.5% for standard backpropagation.>