Engineering and TechnologyNature Communications
Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces
T. Kim, Y. Shin, et al.
Explore a groundbreaking silent speech interface that utilizes advanced crystalline-silicon-based strain sensors, achieving impressive classification accuracy of 87.53%. This innovative research conducted by a team of experts at Yonsei University promises to revolutionize how we interact silently through technology.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency
C. Tang, M. Xu, et al.
Engineering and Technology
Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials
S. Masubuchi, E. Watanabe, et al.
Computer Science
A deep learning framework for gender sensitive speech emotion recognition based on MFCC feature selection and SHAP analysis
Q. Hu, Y. Peng, et al.
Computer Science
Deep learning-based multi-criteria recommender system for technology-enhanced learning
L. Salau, H. Mohamed, et al.
