On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems

arXiv:2403.10549v1 Announce Type: cross
Abstract: 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 …

abstract accuracy arxiv cs.lg cs.sd domain edge eess.as embedded environments loss low networks neural networks noise on-device learning power process systems the edge type