Denby, B. et al. Silent speech interfaces. Speech Commun. 52, 270\u2013287 (2010). This comprehensive review formalizes SSIs as a research field.<\/p>\n
Gonzalez-Lopez, J. A. et al. Silent speech interfaces for speech restoration: a review. IEEE Access 8, 177995\u2013178021 (2020).<\/p>\n Guenther, F. H. et al. in Speech Motor Control in Normal and Disordered Speech 29\u201349 (Oxford Univ. Press, 2004).<\/p>\n Silva, A. B. et al. The speech neuroprosthesis. Nat. Rev. Neurosci. 25, 473\u2013492 (2024).<\/p>\n PubMed<\/a>\u00a0 Afouras, T. et al. Deep audio-visual speech recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44, 8717\u20138727 (2022).<\/p>\n PubMed<\/a>\u00a0 Jin, Y. et al. EarCommand: \u201chearing\u201d your silent speech commands in ear. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 1\u201328 (2022).<\/p>\n Liu, H. et al. An epidermal sEMG tattoo-like patch as a new human\u2013machine interface for patients with loss of voice. Microsyst. Nanoeng. 6, 16 (2020). This study introduces flexible surface EMG sensors to silent-speech-related human\u2013machine interfaces.<\/p>\n PubMed<\/a>\u00a0 Kim, T. et al. Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces. Nat. Commun. 13, 5815 (2022).<\/p>\n PubMed<\/a>\u00a0 Liu, S. et al. A data-efficient and easy-to-use lip language interface based on wearable motion capture and speech movement reconstruction. Sci. Adv. 10, eado9576 (2024).<\/p>\n PubMed<\/a>\u00a0 Card, N. S. et al. An accurate and rapidly calibrating speech neuroprosthesis. N. Engl. J. Med. 391, 609\u2013618 (2024).<\/p>\n PubMed<\/a>\u00a0 Willett, F. R. et al. A high-performance speech neuroprosthesis. Nature 620, 1031\u20131036 (2023).<\/p>\n PubMed<\/a>\u00a0 Sugie, N. & Tsunoda, K. A speech prosthesis employing a speech synthesizer: vowel discrimination from perioral muscle activities. IEEE Trans. Biomed. Eng. 32, 485\u2013490 (1985).<\/p>\n PubMed<\/a>\u00a0 Petajan, E. D. Automatic lipreading to enhance speech recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 40\u201347 (IEEE, 1985). This conference paper describes an off-body silent speech decoding system using camera-based automatic lipreading.<\/p>\n Suppes, P., Lu, Z.-L. & Han, B. Brain wave recognition of words. Proc. Natl Acad. Sci. USA 94, 14965\u201314969 (1997).<\/p>\n PubMed<\/a>\u00a0 Assael, Y. M., Shillingford, B., Whiteson, S. & de Freitas, N. LipNet: end-to-end sentence-level lipreading. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1611.01599<\/a> (2016).<\/p>\n Wand, M., Koutn\u00edk, J. & Schmidhuber, J. Lipreading with long short-term memory. In Proc. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing 6115\u20136119 (IEEE, 2016).<\/p>\n Su, Z., Fang, S. & Rekimoto, J. LipLearner: customizable silent speech interactions on mobile devices. In Proc. 2023 CHI Conference on Human Factors in Computing Systems 1\u201321 (ACM, 2023).<\/p>\n Pandey, L. & Arif, A. S. LipType: a silent speech recognizer augmented with an independent repair model. In Proc. 2021 CHI Conference on Human Factors in Computing Systems 1\u201319 (ACM, 2021).<\/p>\n Pandey, L. & Arif, A. S. MELDER: the design and evaluation of a real-time silent speech recognizer for mobile devices. In Proc. CHI Conference on Human Factors in Computing Systems 1\u201323 (ACM, 2024).<\/p>\n Wang, X., Su, Z., Rekimoto, J. & Zhang, Y. Watch your mouth: silent speech recognition with depth sensing. In Proc. CHI Conference on Human Factors in Computing Systems 1\u201315 (ACM, 2024).<\/p>\n Chen, T. et al. C-Face: continuously reconstructing facial expressions by deep learning contours of the face with ear-mounted miniature cameras. In Proc. 33rd Annual ACM Symposium on User Interface Software and Technology 112\u2013125 (ACM, 2020).<\/p>\n Zhang, R. et al. SpeeChin: a smart necklace for silent speech recognition. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 1\u201323 (2021).<\/p>\n CAS<\/a>\u00a0 Kimura, N., Kono, M. & Rekimoto, J. SottoVoce: an ultrasound imaging-based silent speech interaction using deep neural networks. In Proc. 2019 CHI Conference on Human Factors in Computing Systems 1\u201311 (ACM, 2019).<\/p>\n Tan, J., Nguyen, C.-T. & Wang, X. SilentTalk: lip reading through ultrasonic sensing on mobile phones. In Proc. IEEE INFOCOM 2017\u2014IEEE Conference on Computer Communications 1\u20139 (IEEE, 2017).<\/p>\n Gao, Y. et al. EchoWhisper: exploring an acoustic-based silent speech interface for smartphone. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 1\u201327 (2020).<\/p>\n Zhang, Q. et al. SoundLip: enabling word and sentence-level lip interaction for smart devices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 1\u201328 (2021).<\/p>\n CAS<\/a>\u00a0 Zhang, R. et al. EchoSpeech: continuous silent speech recognition on minimally-obtrusive eyewear powered by acoustic sensing. In Proc. 2023 CHI Conference on Human Factors in Computing Systems 1\u201318 (ACM, 2023).<\/p>\n Dong, X. et al. ReHEarSSE: recognizing hidden-in-the-ear silently spelled expressions. In Proc. CHI Conference on Human Factors in Computing Systems 1\u201316 (ACM, 2024).<\/p>\n Zhang, R. et al. HPSpeech: silent speech interface for commodity headphones. In Proc. 2023 International Symposium on Wearable Computers 60\u201365 (ACM, 2023).<\/p>\n Kwon, J. et al. Novel three-axis accelerometer-based silent speech interface using deep neural network. Eng. Appl. Artif. Intell. 120, 105909 (2023).<\/p>\n Zhou, Q. et al. Triboelectric nanogenerator-based sensor systems for chemical or biological detection. Adv. Mater. 33, 2008276 (2021).<\/p>\n CAS<\/a>\u00a0 Wang, Z. L. On Maxwell\u2019s displacement current for energy and sensors: the origin of nanogenerators. Mater. Today 20, 74\u201382 (2017).<\/p>\n Lu, Y. et al. Decoding lip language using triboelectric sensors with deep learning. Nat. Commun. 13, 1401 (2022). This paper reports on a triboelectric-sensor-based silent speech decoding system.<\/p>\n PubMed<\/a>\u00a0 De Luca, C. J. The use of surface electromyography in biomechanics. J. Appl. Biomech. 13, 135\u2013163 (1997).<\/p>\n Phinyomark, A. et al. Feature extraction and selection for myoelectric control based on wearable EMG sensors. Sensors 18, 1615 (2013).<\/p>\n Wang, Y. et al. All-weather, natural silent speech recognition via machine-learning-assisted tattoo-like electronics. npj Flex.Electron. 5, 20 (2021).<\/p>\n Tang, C. et al. Wireless silent speech interface using multi-channel textile EMG sensors integrated into headphones. IEEE Trans. Instrum. Meas. 74, 1\u201310 (2025).<\/p>\n Lipomi, D. J. et al. Skin-like pressure and strain sensors based on transparent elastic films of carbon nanotubes. Nat. Nanotechnol. 6, 788\u2013792 (2011).<\/p>\n PubMed<\/a>\u00a0 Amjadi, M. et al. Stretchable, skin-mountable, and wearable strain sensors and their potential applications: a review. Adv. Funct. Mater. 26, 1678\u20131698 (2016).<\/p>\n CAS<\/a>\u00a0 Xu, S. et al. Force-induced ion generation in zwitterionic hydrogels for a sensitive silent-speech sensor. Nat. Commun. 14, 219 (2023).<\/p>\n PubMed<\/a>\u00a0 Yang, Q. et al. Mixed-modality speech recognition and interaction using a wearable artificial throat. Nat. Mach. Intell. 5, 169\u2013180 (2023).<\/p>\n Tang, C. et al. Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency. npj Flex. Electron. 8, 27 (2024).<\/p>\n Tang, C. et al. Wearable intelligent throat enables natural speech in stroke patients with dysarthria. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2411.18266<\/a> (2024). This on-body sensing study demonstrates generalized silent speech decoding directly in patients with speech impairment.<\/p>\n Farwell, L. A. & Donchin, E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70, 510\u2013523 (1988).<\/p>\n PubMed<\/a>\u00a0 Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain\u2013computer interface in humans. Proc. Natl Acad. Sci. USA 101, 17849\u201317854 (2004).<\/p>\n PubMed<\/a>\u00a0 Kaongoen, N., Choi, J. & Jo, S. Speech-imagery-based brain\u2013computer interface system using ear-EEG. J. Neural Eng. 18, 016023 (2021).<\/p>\n PubMed<\/a>\u00a0 Wang, Z. et al. Conformal in-ear bioelectronics for visual and auditory brain\u2013computer interfaces. Nat. Commun. 14, 4213 (2023).<\/p>\n PubMed<\/a>\u00a0 Occhipinti, E., Davies, H. J., Hammour, G. & Mandic, D. P. Hearables: artefact removal in ear-EEG for continuous 24\/7 monitoring. In Proc. 2022 International Joint Conference on Neural Networks 1\u20136 (IEEE, 2022).<\/p>\n Mandic, D. P. et al. In your ear: a multimodal hearables device for the assessment of the state of body and mind. IEEE Pulse 14, 17\u201323 (2023).<\/p>\n Cooney, C. et al. A bimodal deep learning architecture for EEG-fNIRS decoding of overt and imagined speech. IEEE Trans. Biomed. Eng. 69, 1983\u20131994 (2021).<\/p>\n D\u00e9fossez, A. et al. Decoding speech perception from non-invasive brain recordings. Nat. Mach. Intell. 5, 1097\u20131107 (2023).<\/p>\n Leuthardt, E. C. et al. A brain\u2013computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63\u201371 (2004).<\/p>\n PubMed<\/a>\u00a0 Schalk, G. & Leuthardt, E. C. Brain\u2013computer interfaces using electrocorticographic signals. IEEE Rev. Biomed. Eng. 4, 140\u2013154 (2011).<\/p>\n PubMed<\/a>\u00a0 Angrick, M. et al. Speech synthesis from ECoG using densely connected 3D convolutional neural networks. J. Neural Eng. 16, 036019 (2019).<\/p>\n PubMed<\/a>\u00a0 Moses, D. A. et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N. Engl. J. Med. 385, 217\u2013227 (2021).<\/p>\n PubMed<\/a>\u00a0 Metzger, S. L. et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620, 1037\u20131046 (2023).<\/p>\n PubMed<\/a>\u00a0 Littlejohn, K. T. et al. A streaming brain-to-voice neuroprosthesis to restore naturalistic communication. Nat. Neurosci. 28, 1\u201311 (2025).<\/p>\n Duraivel, S. et al. High-resolution neural recordings improve the accuracy of speech decoding. Nat. Commun. 14, 6938 (2023).<\/p>\n PubMed<\/a>\u00a0 Munari, C. et al. Stereo-electroencephalography methodology: advantages and limits. Acta Neurol. Scand. 89, 56\u201367 (1994).<\/p>\n Mullin, J. P. et al. Is SEEG safe? A systematic review and meta-analysis of stereo-electroencephalography-related complications. Epilepsia 57, 386\u2013401 (2016).<\/p>\n PubMed<\/a>\u00a0 Abarrategui, B. et al. New stimulation procedures for language mapping in stereo-EEG. Epilepsia 65, 1720\u20131729 (2024).<\/p>\n PubMed<\/a>\u00a0 He, T. et al. VocalMind: a stereotactic EEG dataset for vocalized, mimed, and imagined speech in tonal language. Sci. Data 12, 657 (2025).<\/p>\n PubMed<\/a>\u00a0 Schwartz, A. B. et al. Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron 52, 205\u2013220 (2006).<\/p>\n PubMed<\/a>\u00a0 Flint, R. D. et al. Accurate decoding of reaching movements from field potentials in the human motor cortex. J. Neural Eng. 9, 046006 (2012).<\/p>\n PubMed<\/a>\u00a0 Mugler, E. M. et al. Direct classification of all American English phonemes using signals from functional speech motor cortex. J. Neural Eng. 11, 035015 (2014).<\/p>\n PubMed<\/a>\u00a0 Willett, F. R. et al. High-performance brain-to-text communication via handwriting decoding. Nature 593, 249\u2013254 (2021).<\/p>\n PubMed<\/a>\u00a0 Matsumura, G. et al. Real-time personal healthcare data analysis using edge computing for multimodal wearable sensors. Device 3, 100597 (2025).<\/p>\n Liu, T. et al. Machine learning-assisted wearable sensing systems for speech recognition and interaction. Nat. Commun. 16, 2363 (2025).<\/p>\n PubMed<\/a>\u00a0 Cai, D. et al. SILENCE: protecting privacy in offloaded speech understanding on resource-constrained devices. Adv. Neural Inform. Proc. Syst. 37, 105928\u2013105948 (2024).<\/p>\n Deng, Z. et al. Silent speech recognition based on surface electromyography using a few electrode sites under the guidance from high-density electrode arrays. IEEE Trans. Instrum. Meas. 72, 1\u201311 (2023).<\/p>\n Pang, C. et al. A flexible and highly sensitive strain-gauge sensor using reversible interlocking of nanofibres. Nat. Mater. 11, 795\u2013801 (2012).<\/p>\n PubMed<\/a>\u00a0 Tang, C. et al. A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life. Proc. Natl Acad. Sci. USA 122, e2420498122 (2025).<\/p>\n PubMed<\/a>\u00a0 Xu, M. et al. Simultaneous isotropic omnidirectional hypersensitive strain sensing and deep learning-assisted direction recognition in a biomimetic stretchable device. Adv. Mater. 37, 2420322 (2025).<\/p>\n PubMed<\/a>\u00a0 Gong, S. et al. Hierarchically resistive skins as specific and multimetric on-throat wearable biosensors. Nat. Neurosci. 18, 889\u2013897 (2023).<\/p>\n CAS<\/a>\u00a0 Xue, C. et al. A CMOS-integrated compute-in-memory macro based on resistive random-access memory for AI edge devices. Nat. Electron. 4, 81\u201390 (2021).<\/p>\n CAS<\/a>\u00a0 Fei, N. et al. Towards artificial general intelligence via a multimodal foundation model. Nat. Commun. 13, 3094 (2022).<\/p>\n PubMed<\/a>\u00a0 Roy, K. et al. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607\u2013617 (2019).<\/p>\n PubMed<\/a>\u00a0 Wang, S. et al. Memristor-based adaptive neuromorphic perception in unstructured environments. Nat. Commun. 15, 4671 (2024).<\/p>\n PubMed<\/a>\u00a0 Tang, C. et al. A roadmap for the development of human body digital twins. Nat. Rev. Elect. Eng. 1, 199\u2013207 (2024).<\/p>\n Zinn, S. et al. The effect of poststroke cognitive impairment on rehabilitation process and functional outcome. Arch. Phys. Med. Rehab. 85, 1084\u20131090 (2004).<\/p>\n Kennedy, P. R. & Bakay, R. A. E. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9, 1707\u20131711 (1998). This paper reports on a patient-level in-body neural interface demonstrating direct brain-connected communication in a paralysed individual.<\/p>\n PubMed<\/a>\u00a0 Denby, B. & Stone, M. Speech synthesis from real-time ultrasound images of the tongue. In Proc. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing 685\u2013688 (IEEE, 2004). This conference paper demonstrates silent speech synthesis using real-time ultrasound imaging of the tongue.<\/p>\n Galatas, G., Potamianos, G. & Makedon, F. Audio-visual speech recognition incorporating facial depth information captured by the Kinect. In Proc. 20th European Signal Processing Conference 2714\u20132717 (IEEE, 2012).<\/p>\n Shin, Y. H. & Seo, J. Towards contactless silent speech recognition based on detection of active and visible articulators using IR-UWB radar. Sensors 16, 1812 (2016).<\/p>\n PubMed<\/a>\u00a0 Sun, K. et al. Lip-Interact: improving mobile device interaction with silent speech commands. In Proc. 31st Annual ACM Symposium on User Interface Software and Technology 581\u2013593 (ACM, 2018).<\/p>\n Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568, 493\u2013498 (2019). This paper reports a neural decoder that synthesizes full spoken sentences from ECoG recordings.<\/p>\n PubMed<\/a>\u00a0 Ravenscroft, D. et al. Machine learning methods for automatic silent speech recognition using a wearable graphene strain gauge sensor. Sensors 22, 299 (2022).<\/p>\n Che, Z. et al. Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system. Nat. Commun. 15, 1873 (2024).<\/p>\n PubMed<\/a>\u00a0
\n Google Scholar<\/a>\u00a0\n <\/p>\n
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\n Google Scholar<\/a>\u00a0\n <\/p>\n","protected":false},"excerpt":{"rendered":"Denby, B. et al. Silent speech interfaces. Speech Commun. 52, 270\u2013287 (2010). This comprehensive review formalizes SSIs as…\n","protected":false},"author":2,"featured_media":375863,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[554,733,4308,18924,3252,6112,22611,11662,11660,86,56,54,55],"class_list":["post-375862","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-biomedical-engineering-biotechnology","tag-biotechnology","tag-computer-science","tag-electrical-and-electronic-engineering","tag-nanotechnology","tag-optical-and-electronic-materials","tag-technology","tag-uk","tag-united-kingdom","tag-unitedkingdom"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/375862","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/comments?post=375862"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/375862\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media\/375863"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media?parent=375862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/categories?post=375862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/tags?post=375862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}