Recognition of Isolated Digits using a Liquid State Machine

The Liquid State Machine (LSM) is a recently developed computational model [1] with interesting properties. It can be used for pattern classification, function approximation and other complex tasks. Contrary to most common computational models, the LSM does not require information to be stored in some stable state of the system: the inherent dynamics of the system are used by a memoryless readout function to compute the output. We apply this framework to the practical task of isolated word speech recognition. We investigate two different speech front ends and different ways of coding the inputs into spike trains. The robustness against noise added to the speech is also briefly researched. It turns out that a biologically realistic configuration of the LSM gives the best result, and that its performance rivals that of a state-of-the-art speech recognition system.