Unstable connectionist networks in speech recognition

Abstract

Connectionist networks evolve in time according to a prescribed rule. Typically, they are designed to be stable so that their temporal activity ceases after a short transient period. However, meaningful patterns in speech have a temporal component: therefore it seems natural to attempt to map the temporality of speech patterns onto the temporality of an unstable network. The authors have begun some exploratory experiments to train networks to recognise temporal patterns. They have designed fully connected networks that are trained to emulate and classify sequences by regarding each temporal state of a network as a layer in a feedforward network. Training is then performed by a variant of the back-propagation algorithm. They have conducted initial experiments using the output of a peripheral auditory model.