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.
