The Stony Brook University provided guidelines for study procedures. Informed consent was obtained from all participants. After data acquisition, the data were segmented into every single trial, and features were calculated. LDA/SVM training programs were applied to get the classification results. Figures 5 and 6 and Figures S14-S21, Supporting Information summarize the recognition results of all four subjects. The results of 11 words from different viseme groups using the LDA and the SVM were 94.8% ± 0.035 and 90.5% ± 0.061, respectively. The results for nine pairs of similar words were 86.8% ± 0.053 and 82.6% ± 0.056 when using the LDA and the SVM, respectively. The recognition accuracy was represented as mean ± standard derivation. Matlab was used as the software for the statistical analysis.

In article number 2205058, Petar M. Djurić, Shanshan Yao, and co-workers present the design of a truly natural and robust electromyogram-based lip-reading system that can capture speech-relevant lip gestures and decode lip movements for speech. The system allows for a mechanically imperceptible, skin-friendly, visually unobtrusive platform for tracking and interpreting lip movements with minimal interference.