On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge
  Embedded Systems
Cristian Cioflan Lukas Cavigelli Manuele Rusci Miguel de Prado Luca Benini
Keyword spotting accuracy degrades when neural networks are exposed to noisy
environments. On-site adaptation to previously unseen noise is crucial to
recovering accuracy loss, and on-device learning is required to ensure that the
adaptation process happens entirely on the edge device. In this work, we
propose a fully on-device domain adaptation system achieving up to 14% accuracy
gains over already-robust keyword spotting models. We enable on-device learning
with less than 10 kB of memory, using only 100 labeled utterances to recover 5%
accuracy after adapting to the complex speech noise. We demonstrate that domain
adaptation can be achieved on ultra-low-power microcontrollers with as little
as 806 mJ in only 14 s on always-on, battery-operated devices.