  In this work, we propose Tiny-CRNN (Tiny Convolutional Recurrent Neural
Network) models applied to the problem of wakeword detection, and augment them
with scaled dot product attention. We find that, compared to Convolutional
Neural Network models, False Accepts in a 250k parameter budget can be reduced
by 25% with a 10% reduction in parameter size by using models based on the
Tiny-CRNN architecture, and we can get up to 32% reduction in False Accepts 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 this architecture, as
well as differences in start-end index errors and latency in comparison to CNN,
DNN, and DNN-HMM models.

Machine Learning;Sound;Audio and Speech Processing