Neural nets for speech recognition

Artificial neural networks are of interest for two main reasons. First, they provide architectures to implement many algorithms used in speech rccognizcrs with fine grain massive parallelism. Second, they are leading to new computational algorithms and new approaches to speech recognition inspired by biological nervous systems. Neural net approaches to the problems of speech preprocessing, pattern classification, and time alignment are reviewed. Preprocessors using auditory nerve time‐synchrony models have provided improved recognition performance in noise [O. Ghitza, ICASSP 87, 2372–2375]. Highly parallel neural net architectures exist to implement many important traditional classification algorithms, such as k‐nearest neighbor and Gaussian classifiers [R. Lippmann, IEEE ASSP Mag. 4(2), 4–22 (1987)]. Newer multilayer perceptron classifiers trained with back propagation can form arbitrary decision regions, are robust, and train rapidly for convex decision regions. These nets performed as well as conventio...