Neural Network Classifiers for Speech Recognition

Neural nets offer an approach to computation thatmimics biological nervous systems. Algorithms based on neural nets have been proposed to address speech recognition tasks which humans perlorm with little apparent effort. In this paper, neural net classifiers are described and compared with conventional classification algorithms. Perceptron classifiers trained with a new algorithm, called back propagation, were tested and found to perform roughly as well as conventional classifiers on digit and vowel classification tasks. A new net architecture, called a Viterbi net, which recognizes time-varying input patterns, provided an accuracy ofbetter than 99% on a large speech data base. Perceptrons and another neural net, the feature map, were implemented in a very large-scale integration (VLSI) device.