mmWave-Whisper: Phone Call Eavesdropping and Transcription Using Millimeter-Wave Radar
Suryoday Basak, Abhijeeth Padarthi, Mahanth Gowda

TL;DR
mmWave-Whisper demonstrates a novel radar-based system capable of eavesdropping on phone calls and transcribing speech remotely, achieving full-vocabulary recognition using millimeter-wave radar and advanced speech models.
Contribution
This work is the first to enable full-sentence speech recognition from smartphone earpiece vibrations using millimeter-wave radar and synthetic training data techniques.
Findings
Achieves 44.74% word accuracy in real-world scenarios
Operates effectively within 25-125 cm range
Utilizes synthetic data and domain adaptation for training
Abstract
This paper introduces mmWave-Whisper, a system that demonstrates the feasibility of full-corpus automated speech recognition (ASR) on phone calls eavesdropped remotely using off-the-shelf frequency modulated continuous wave (FMCW) millimeter-wave radars. Operating in the 77-81 GHz range, mmWave-Whisper captures earpiece vibrations from smartphones, converts them into audio, and processes the audio to produce speech transcriptions automatically. Unlike previous work that focused on loudspeakers or limited vocabulary, this is the first work to perform such a speech recognition by handling large vocabulary and full sentences on earpiece vibrations from smartphones. This approach expands the potential of radar-audio eavesdropping. mmWave-Whisper addresses challenges such as the lack of large scale training datasets, low SNR, and limited frequency information in radar data through a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Linguistic research and analysis
