แนวคิดหลัก
Fully on-device domain adaptation system achieves significant accuracy gains in noisy environments for keyword spotting models.
บทคัดย่อ
Keyword spotting accuracy can degrade in noisy environments, necessitating on-site adaptation. This work proposes a fully on-device domain adaptation system that achieves up to 14% accuracy gains over robust keyword spotting models. The system enables on-device learning with minimal memory and labeled utterances, showcasing the ability to recover accuracy after adapting to complex speech noise. Domain adaptation is demonstrated on ultra-low-power microcontrollers with efficient energy consumption. The study addresses noise-robustness, low-power microcontrollers, extreme edge computing, TinyML, and keyword spotting.
สถิติ
We enable on-device learning with less than 10 kB of memory.
Achieving up to 14% accuracy gains over already-robust keyword spotting models.
Demonstrated domain adaptation can be achieved on ultra-low-power microcontrollers with as little as 806 mJ in only 14 s.
คำพูด
"We propose a fully on-device domain adaptation system achieving up to 14% accuracy gains over already-robust keyword spotting models."
"We demonstrate that domain adaptation can be achieved on ultra-low-power microcontrollers with as little as 806 mJ in only 14 s."