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arXiv · 2403.10549

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
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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.

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Cristian Cioflan, Lukas Cavigelli, Manuele Rusci, Miguel de Prado, Luca Benini. 2024-03-12. On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems. https://arxiv.org/abs/2403.10549

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