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arXiv · 2512.16518

Poster: Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning

Xuefu Dong·Liqiang Xu·Lixing He·Zengyi Han·Ken Christofferson·Yifei Chen·Akihito Taya·Yuuki Nishiyama·Kaoru Sezaki
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Abstract

Silent speech interface (SSI) enables hands-free input without audible vocalization, but most SSI systems do not verify speaker identity. We present HEar-ID, which uses consumer active noise-canceling earbuds to capture low-frequency "whisper" audio and high-frequency ultrasonic reflections. Features from both streams pass through a shared encoder, producing embeddings that feed a contrastive branch for user authentication and an SSI head for silent spelling recognition. This design supports decoding of 50 words while reliably rejecting impostors, all on commodity earbuds with a single model. Experiments demonstrate that HEar-ID achieves strong spelling accuracy and robust authentication.

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Xuefu Dong, Liqiang Xu, Lixing He, Zengyi Han, Ken Christofferson, Yifei Chen, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki. 2025-12-18. Poster: Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning. https://doi.org/10.1145/3714394.3754429

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