Lip-Siri: Contactless Open-Sentence Silent Speech with Wi-Fi Backscatter
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915755664080896 |
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| author | Tian, Ye Du, Haohua Gu, Chao Zhang, Junyang Wang, Shanyue Zhou, Hao Hou, Jiahui Li, Xiang-Yang |
| author_facet | Tian, Ye Du, Haohua Gu, Chao Zhang, Junyang Wang, Shanyue Zhou, Hao Hou, Jiahui Li, Xiang-Yang |
| contents | Silent speech interfaces (SSIs) enable silent interaction in noise-sensitive or privacy-sensitive settings. However, existing SSIs face practical deployment trade-offs among privacy, user experience, and energy consumption, and most remain limited to closed-set recognition over small, pre-defined vocabularies of words or sentences, which restricts real-world expressiveness. In this paper, we present Lip-Siri, to the best of our knowledge, the first Wi-Fi backscatter--based SSI that supports open-vocabulary sentence recognition via lexicon-guided subword decoding. Lip-Siri designs a frequency-shifted backscatter tag to isolate tag-modulated reflections and suppress interference from non-target motions, enabling reliable extraction of lip-motion traces from ubiquitous Wi-Fi signals. We then segment continuous traces into lip-motion units, cluster them, learn robust unit representations via cluster-based self-supervision, and finally propose a lexicon-guided Transformer encoder--decoder with beam search to decode variable-length sentence sequences. We implement an end-to-end prototype and evaluate it with 15 participants on 340 sentences and 3,398 words across multiple scenarios. Lip-Siri achieves 85.61% accuracy on word prediction and a WER of 36.87% on continuous sentence recognition, approaching the performance of representative vision-based lip-reading systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_18177 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Lip-Siri: Contactless Open-Sentence Silent Speech with Wi-Fi Backscatter Tian, Ye Du, Haohua Gu, Chao Zhang, Junyang Wang, Shanyue Zhou, Hao Hou, Jiahui Li, Xiang-Yang Human-Computer Interaction Silent speech interfaces (SSIs) enable silent interaction in noise-sensitive or privacy-sensitive settings. However, existing SSIs face practical deployment trade-offs among privacy, user experience, and energy consumption, and most remain limited to closed-set recognition over small, pre-defined vocabularies of words or sentences, which restricts real-world expressiveness. In this paper, we present Lip-Siri, to the best of our knowledge, the first Wi-Fi backscatter--based SSI that supports open-vocabulary sentence recognition via lexicon-guided subword decoding. Lip-Siri designs a frequency-shifted backscatter tag to isolate tag-modulated reflections and suppress interference from non-target motions, enabling reliable extraction of lip-motion traces from ubiquitous Wi-Fi signals. We then segment continuous traces into lip-motion units, cluster them, learn robust unit representations via cluster-based self-supervision, and finally propose a lexicon-guided Transformer encoder--decoder with beam search to decode variable-length sentence sequences. We implement an end-to-end prototype and evaluate it with 15 participants on 340 sentences and 3,398 words across multiple scenarios. Lip-Siri achieves 85.61% accuracy on word prediction and a WER of 36.87% on continuous sentence recognition, approaching the performance of representative vision-based lip-reading systems. |
| title | Lip-Siri: Contactless Open-Sentence Silent Speech with Wi-Fi Backscatter |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2601.18177 |