Beyond Microphone: mmWave-Based Interference-Resilient Voice Activity Detection

Abstract

Microphone-based voice activity detection systems usually require hotword detection and they cannot perform well under the presence of interference and noise. Users attending online meetings in noisy environments usually mute and unmute their microphones manually due to the limited performance of interference-resilient VAD. In order to automate voice detection in challenging environments without dictionary limitations, we explore beyond microphones and propose to use mmWave-based sensing, which is already available in many smart phones and IoT devices. Our preliminary experiments in multiple places with several users indicate that mmWave-based VAD can match and surpass the performance of an audio-based VAD in noisy conditions, while being robust against interference.