Efficient On-Device Domain Learning for Keyword Spotting on Ultra-Low-Power Platforms

Deep Neural Network-based Keyword Spotting accuracy degrades in noisy environments. On-site adaptation to previously unseen noise is crucial to recover accuracy loss, and on-device learning is required in scenarios where adaptation has to happen in the field. In this work, we propose a fully on-device domain adaptation system, enabling edge devices to achieve noise-robust keyword spotting. We achieve up to 14% accuracy gains over already-robust keyword spotting models, and up to 21% increments when evaluating our methodology on keyword datasets disjoint from the offline training set. In extreme edge scenarios where Keyword Spotting is critical, using as little as 10 kB of memory and only 100 labeled utterances, we enable on-device learning and demonstrate accuracy recovery of up to 5% after adapting to complex, non-stationary speech noise. We show that domain adaptation can be achieved on ultra-low-power microcontrollers with as little as 357 mJ within 14 seconds on always-on, battery-operated devices. This work is the first to demonstrate an end-to-end on-device domain adaptation system for noise robust keyword spotting models on ultra-low-power, extreme edge platforms.