On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems
<|reference_start|>On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems: 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.<|reference_end|>
@article{cioflan2024on-device,
    title={On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge
  Embedded Systems},
    author={Cristian Cioflan, Lukas Cavigelli, Manuele Rusci, Miguel de Prado,
  Luca Benini},
    journal={arXiv preprint arXiv:2403.10549},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10549},
    primaryClass={cs.SD cs.LG eess.AS}
}