用耳机同时识别静默输入和用户身份，一模型搞定。
Poster: Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning
- 双流特征融合：低频耳语+高频超声反射
- 50词静默输入准确率高，拒假率强
- 仅用普通降噪耳机，无需额外设备
静默语音接口（SSI）实现无声音输入，但多数系统缺乏身份验证。本文提出HEar-ID，利用消费级主动降噪耳机捕捉低频‘耳语’音频与高频超声反射信号。两条流的特征经共享编码器处理，生成嵌入向量，分别接入对比学习分支用于用户认证，以及SSI头用于静默拼写识别。该设计支持50词解码，可在普通耳机上实现可靠拒假，实验表明其兼具高拼写准确率与强认证鲁棒性。
原文摘要 · Abstract (English)
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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