Proceedings of the Annual Meeting of the Cognitive Science Society

We describe a technique for automatically adapt- ing to the rate of an incoming signal. We first build a model of the signal using a recurrent net- work trained to predict the input at some delay, for a "typical" rate of the signal. Then, fixing the weights of this network, we adapt the time con- stant T of the network using gradient descent, adapting the delay appropriately as well. W e have found that on simple signals, the network adapts rapidly to new inputs varying in rate from twice as fast as the original signal, d o w n to ten times as slow. So far our results are based on linear rate changes. We discuss the possibilities of application to speech.