  We present a system for keyword spotting that, except for a frontend
component for feature generation, it is entirely contained in a deep neural
network (DNN) model trained "end-to-end" to predict the presence of the keyword
in a stream of audio. The main contributions of this work are, first, an
efficient memoized neural network topology that aims at making better use of
the parameters and associated computations in the DNN by holding a memory of
previous activations distributed over the depth of the DNN. The second
contribution is a method to train the DNN, end-to-end, to produce the keyword
spotting score. This system significantly outperforms previous approaches both
in terms of quality of detection as well as size and computation.
