Speech classification using a modified focused gamma network

A modified version of the focused gamma network is proposed and applied to speech classification. Each input neuron of the network is equipped with a pair of gamma memories which have an adjustable time-scale parameter. In addition, each channel of speech features is equally divided into several segments, and each segment is clocked into an individual input neuron. With these modifications, the network is able to develop a proper time scale for each segment of patterns and handle speech patterns of different lengths as well.