Tiny-crnn: Streaming wakeword detection in a low footprint setting

Mohammad Omar Khursheed, Santosh Kumar Cheekatmalla, Christin Jose, Brian Kulis, Gengshen Fu, Rajath Kumar

Abstract:

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.