A connectionist approach to speech recognition using peripheral auditory modelling

Abstract

A prototype isolated word recogniser was constructed, with an auditory-based analysis component and a pattern classification module based on a parallel distributed processing paradigm. The auditory model used was a band-pass non-linear (BPNL) configuration which incorporates the effects of lateral suppression. Pattern classification was performed by a layered, feed-forward neural network, consisting of an array of input nodes representing the binary features output by the auditory model, a set of hidden nodes and an array of output nodes representing the word to be recognised. A suitable internal representation was learned by the method of back-propagation of errors by gradient descent using the generalised delta rule. This prototype recogniser was trained to recognise English digits spoken by male and female speakers. Recognition rates for the digit set, (zero to ten) were better than 80%.