In 2008, Lee further expanded the number of words to 60, using the hidden Markov model to achieve 87.07% accuracy 10. recognized six independent words with 92% accuracy 9. In the past two decades, the number of classified words has been increasing. Sugie used three-channel electrodes to classify five Japanese vowels, while Morse successfully separated two English words with 97% accuracy. Sugie 7 of Japan and Morse 8 of the United States published their research almost at the same time. Silent speech recognition based on EMG can be traced back to the mid-1980s. Such complexity of sEMG motivates researchers to develop various classifiers, such as supporting vector machine (SVM) 2, deep learning 3, 4, and machine learning 5, 6, to construct the mapping relations between facial sEMG and silent speech. The diversity of sEMG even occurs in the same actions 1. SEMG signals are ubiquitously distributed on skins and have significant spatiotemporal variability 1. Natural silent speech in daily and working life hinges on high-fidelity sEMG acquisition, accuracy classification, and imperceptible wearable devices. Human brains manipulate the voices by neural signals, and therefore it is effective to learn about human intentions by recognition of surface electromyographic (sEMG) signals on faces. Speaking is owned from babyhood, thus silent speech requires less specialized learning and carries more information than most silent alternatives (from typing to sign language to Morse code). More importantly, compared to voice interactions or visual interactions, human–machine interactions using silent speech are versatile enough to work in all-weather surroundings, such as obscured, dynamic, quiet, dark, and noisy. Silent speech can offer people with aphasia an alternative communication way. We successfully apply the SSRS to 1-day routine life, including daily greeting, running, dining, manipulating industrial robots in deafening noise, and expressing in darkness, which shows great promotion in real-world applications. A series of experiments show that the silent speech recognition system (SSRS) can enduringly comply with large deformation (~45%) of faces by virtue of the electricity-preferred tattoo-like electrodes and recognize up to 110 words covering daily vocabularies with a high average accuracy of 92.64% simply by use of small-sample machine learning. In the strategy, the tattoo-like electronics imperceptibly attached on facial skin record high-quality bio-data of various silent speech, and the machine-learning algorithm deployed on the cloud recognizes accurately the silent speech and reduces the weight of the wireless acquisition module. The strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily environments. This paper develops a silent speech strategy to achieve all-weather, natural interactions. The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various disturbances.
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