Signed languages are not as pervasive a conversational medium as spoken languages due to the history of institutional suppression of the former and the linguistic hegemony of the latter. This has led to a communication barrier between signers and non-signers that could be mitigated by technology-mediated approaches. Here, we show that a wearable sign-to-speech translation system, assisted by machine learning, can accurately translate the hand gestures of American Sign Language into speech. The wearable sign-to-speech translation system is composed of yarn-based stretchable sensor arrays and a wireless printed circuit board, and offers a high sensitivity and fast response time, allowing real-time translation of signs into spoken words to be performed. By analysing 660 acquired sign language hand gesture recognition patterns, we demonstrate a recognition rate of up to 98.63% and a recognition time of less than 1 s.
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The data that support the plots within this paper and other findings of the study are available from the corresponding authors upon reasonable request.
The code is available from the corresponding authors upon reasonable request.
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J.Y. acknowledges support from the National Natural Science Foundation of China (no. 51675069), the Fundamental Research Funds for the Central Universities (nos. 2018CDQYGD0020 and cqu2018CDHB1A05), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1703047) and the Natural Science Foundation Projects of Chongqing (cstc2017shmsA40018 and cstc2018jcyjAX0076). J.C. also acknowledges the Henry Samueli School of Engineering & Applied Science and the Department of Bioengineering at the University of California, Los Angeles for start-up support. We also thank B. Lewis in the Department of Linguistics at the University of California, Los Angeles for his inspiring discussion on avoiding using a deficit-based framework in the description of deaf people and sign language in our Article.
J.C., J.Y. and Z.Z. have filed a patent based on this work under the US provisional patent application no. 62/967,509.
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Zhou, Z., Chen, K., Li, X. et al. Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays. Nat Electron 3, 571–578 (2020). https://doi.org/10.1038/s41928-020-0428-6
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