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Abstract
This paper introduces a novel silent speech interface (SSI) using crystalline-silicon-based strain sensors and a 3D convolutional deep learning algorithm. Minimized strain gauges (<0.1 mm²) effectively capture biaxial strain information. Four sensors near the mouth classified 100 words with 87.53% accuracy, outperforming an sEMG-based SSI (42.60%) with similar sensor dimensions. The system's reliability was verified through various analyses.
Publisher
Nature Communications
Published On
Oct 03, 2022
Authors
Taemin Kim, Yejee Shin, Kyowon Kang, Kiho Kim, Gwanho Kim, Yunsu Byeon, Hwayeon Kim, Yuyan Gao, Jeong Ryong Lee, Geonhui Son, Taeseong Kim, Yohan Jun, Jihyun Kim, Jinyoung Lee, Seyun Um, Yoohwan Kwon, Byung Gwan Son, Myeongki Cho, Mingyu Sang, Jongwoon Shin, Kyubeen Kim, Jungmin Suh, Heekyeong Choi, Seokjun Hong, Huanyu Cheng, Hong-Goo Kang, Dosik Hwang, Ki Jun Yu
Tags
silent speech interface
strain sensors
deep learning
classification accuracy
biometrics

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