Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors
<|reference_start|>Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors: This paper proposes a self-learning framework to incrementally train (fine-tune) a personalized Keyword Spotting (KWS) model after the deployment on ultra-low power smart audio sensors. We address the fundamental problem of the absence of labeled training data by assigning pseudo-labels to the new recorded audio frames based on a similarity score with respect to few user recordings. By experimenting with multiple KWS models with a number of parameters up to 0.5M on two public datasets, we show an accuracy improvement of up to +19.2% and +16.0% vs. the initial models pretrained on a large set of generic keywords. The labeling task is demonstrated on a sensor system composed of a low-power microphone and an energy-efficient Microcontroller (MCU). By efficiently exploiting the heterogeneous processing engines of the MCU, the always-on labeling task runs in real-time with an average power cost of up to 8.2 mW. On the same platform, we estimate an energy cost for on-device training 10x lower than the labeling energy if sampling a new utterance every 5 s or 16.4 s with a DS-CNN-S or a DS-CNN-M model. Our empirical result paves the way to self-adaptive personalized KWS sensors at the extreme edge.<|reference_end|>
@article{rusci2024self-learning,
    title={Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio
  Sensors},
    author={Manuele Rusci, Francesco Paci, Marco Fariselli, Eric Flamand, Tinne
  Tuytelaars},
    journal={arXiv preprint arXiv:2408.12481},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12481},
    primaryClass={cs.SD cs.LG eess.AS}
}