Voice interfaces have become one of the most ubiquitous human–computer interaction methods in recent years. Voice activity detection (VAD) is typically the first building block of a complex voice interface, often relying on audio signals. Acoustics-based VAD systems do not perform well in noisy and interference-prone environments. Smart assistants mitigate this problem by using a dictionary-based detection system. However, this approach is limited in its applicability. For instance, users may still need to manually mute and unmute their microphones during online meetings to prevent detection of interfering users, and speech leakage. In order to automate voice detection in challenging environments without these limitations, we propose RadioVAD, a noise and interference-resilient VAD system that uses radio modality, which is already available in various smartphones and home assistants. RadioVAD works by detecting possible human presence in the Field of View of the device, extracting the vocal fold’s vibration signal from the target speaker, and utilizing a time-domain neural network on raw radio signals to detect voice activity. Extensive experiments reveal that RadioVAD can detect voice activity in challenging environments with high accuracy and outperforms audio-based VAD when the audio signal has signal-to-noise ratio below 5 dB. Furthermore, RadioVAD reduces false alarm rate in interference-prone environments by 52%–72%, bringing significant improvements to VAD task. RadioVAD lays the foundation for future voice interfaces utilizing radio modality.