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On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems
- Publication Year :
- 2024
-
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 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.<br />Comment: 5 pages, 2 tables, 2 figures. Accepted at IEEE AICAS 2024
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2403.10549
- Document Type :
- Working Paper