  Voice Activity Detection (VAD) is a fundamental module in many audio
applications. Recent state-of-the-art VAD systems are often based on neural
networks, but they require a computational budget that usually exceeds the
capabilities of a small battery-operated device when preserving the performance
of larger models. In this work, we rely on the input from a bone conduction
microphone (BCM) to design an efficient VAD (BC-VAD) robust against residual
non-stationary noises originating from the environment or speakers not wearing
the BCM.We first show that a larger VAD system (58k parameters) achieves
state-of-the-art results on a publicly available benchmark but fails when
running on bone conduction signals. We then compare its variant BC-VAD (5k
parameters and trained on BC data) with a baseline especially designed for a
BCM and show that the proposed method achieves better performances under
various metrics while keeping the realtime processing requirement for a
microcontroller.
