Back-propagation networks for phoneme recognition

The authors describe research on the convergence of the back-propagation learning algorithms and report results on methods for increasing their convergence rate. Reasons for slow convergence are examined. It is shown that adaptive training methods for improving convergence rate result in a marked improvement over the performance of traditional steepest descent and momentum learning algorithms. A phoneme-based application, using the NETtalk neural network architecture, is described