The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various disturbances. This paper develops a silent speech strategy to achieve all-weather, natural interactions. The strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily environments. 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. 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. 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.

无声语音的内部可用性充当失语症患者的翻译器，并在各种干扰下保持人机/人机交互。本文开发了一种无声语音策略，以实现全天候、自然的交互。该策略几乎不需要使用手语等专业技能，但可以在复杂多变的日常环境中准确传递大容量信息。该策略中，隐形附着在面部皮肤上的类似纹身的电子设备记录了各种无声语音的高质量生物数据，部署在云端的机器学习算法准确识别无声语音并减轻了无线采集模块的重量. 一系列实验表明，无声语音识别系统 (SSRS) 凭借电首选纹身样电极，可以持久地适应人脸的大变形（~45%），并以高识别率识别多达 110 个涵盖日常词汇的单词。仅通过使用小样本机器学习，平均准确率为 92.64%。我们成功地将 SSRS 应用到 1 天的日常生活中，包括日常问候、跑步、用餐、在震耳欲聋的噪音中操纵工业机器人、在黑暗中表达，这在现实世界的应用中得到了极大的推广。