  In this work, we propose small footprint Convolutional Recurrent Neural
Network models applied to the problem of wakeword detection and augment them
with scaled dot product attention. We find that false accepts compared to
Convolutional Neural Network models in a 250k parameter budget can be reduced
by 25% with a 10% reduction in parameter size by using CRNNs, and we can get up
to 32% improvement at a 50k parameter budget with 75% reduction in parameter
size compared to word-level Dense Neural Network models. We discuss solutions
to the challenging problem of performing inference on streaming audio with
CRNNs, as well as differences in start-end index errors and latency in
comparison to CNN, DNN, and DNN-HMM models.
