  The recent ubiquitous adoption of remote conferencing has been accompanied by
omnipresent frustration with distorted or otherwise unclear voice
communication. Audio enhancement can compensate for low-quality input signals
from, for example, small true wireless earbuds, by applying noise suppression
techniques. Such processing relies on voice activity detection (VAD) with low
latency and the added capability of discriminating the wearer's voice from
others - a task of significant computational complexity. The tight energy
budget of devices as small as modern earphones, however, requires any system
attempting to tackle this problem to do so with minimal power and processing
overhead, while not relying on speaker-specific voice samples and training due
to usability concerns.
  This paper presents the design and implementation of a custom research
platform for low-power wireless earbuds based on novel, commercial, MEMS
bone-conduction microphones. Such microphones can record the wearer's speech
with much greater isolation, enabling personalized voice activity detection and
further audio enhancement applications. Furthermore, the paper accurately
evaluates a proposed low-power personalized speech detection algorithm based on
bone conduction data and a recurrent neural network running on the implemented
research platform. This algorithm is compared to an approach based on
traditional microphone input. The performance of the bone conduction system,
achieving detection of speech within 12.8ms at an accuracy of 95\% is
evaluated. Different SoC choices are contrasted, with the final implementation
based on the cutting-edge Ambiq Apollo 4 Blue SoC achieving 2.64mW average
power consumption at 14uJ per inference, reaching 43h of battery life on a
miniature 32mAh li-ion cell and without duty cycling.
