  In this paper, we propose neural network models based on the neural ordinary
differential equation (NODE) for small-footprint keyword spotting (KWS). We
present techniques to apply NODE to KWS that make it possible to adopt Batch
Normalization to NODE-based network and to reduce the number of computations
during inference. Finally, we show that the number of model parameters of the
proposed model is smaller by 68% than that of the conventional KWS model.
