This text is written by researchers at BT Laboratories and at universities who have investigated a set of problems in vision, speech and natural language. It presents a diversity of approaches to the solution of these problems, based on a range of neural network paradigms. The book also includes sections on implementation, which discuss some approaches to hardware realization; and architectures, which describe new ideas and algorithms. The findings discussed here should be of interest not only to those working in the applications areas discussed, but also to people working more widely in the field of neural networks. This book should be of interest to working researchers and graduate students in neural networks.

Table of Contents:
Part 1 Vision: image feature location in multi-resolution images using a hierarchy of multi-layer perceptrons, J. Vincent et al; training multi-layer perceptrons for facial feature location - a case study, Jon Waite; the detection of eyes in facial images using radial basis functions, R. Debenham and S. Garth; training and testing of neural net window operators on spatiotemporal image sequences, M. Wright; a neural network feature detector using a multi-resolution pyramid, C. Hand et al; image classification using Gabor representations with a neural net, P. Lisboa. Part 2 Speech: spoken alphabet recognition using multi-layer perceptrons, Philip Woodland; spoken independent vowel recognition, L. Smith and C. Tang; dissection of perceptron structures in speech and speaker recognition, J. Mason and E. Andrews; segmental sub-word unit classification using a multi-layer perceptron, S. Smyth. Part 3 Natural language: a single layer higher order neural net and its applications to context free grammar recognition, P. Wyard and C. Nightingale; functional compositionality and soft preference rules, N. Sharkey; applications of multi-layer perceptrons in text-to-speech synthesis, W. Ainsworth and N. Warren. Part 4 Implementation: finite word length, integer arithmetic multi-layer perceptron modelling for hardware realization, J. Vincent; a VSLI architecture for implementing neural networks with on-chip propagation algorithm learning, D. Myers et al; an opto-electronic neural network processor, N. Barnes et al. Part 5 Architectures: a dynamic topology net, C. Nightingale; the stochastic search network, J. Bishop and P. Torr; node sequence networks, R. Linggard; some dynamical properties of neural networks, S. Olafsson.