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Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system

Engineering and Technology

Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system

Z. Che, X. Wan, et al.

Discover a groundbreaking self-powered wearable system that enables speaking assistance without vocal folds, boasting an impressive 94.68% accuracy through machine learning. This innovation by Ziyuan Che, Xiao Wan, Jing Xu, Chrystal Duan, Tianqi Zheng, and Jun Chen promises to enhance lives for those with vocal fold dysfunction.

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Playback language: English
Abstract
This paper introduces a self-powered wearable sensing-actuation system for assisted speaking without vocal folds. The system, weighing 7.2 g, uses soft magnetoelasticity to capture laryngeal muscle movements, converting them into electrical signals with 94.68% accuracy via machine learning. These signals are then used to produce voice signals, bypassing vocal fold vibration. This technology aims to improve the quality of life for patients with vocal fold dysfunction.
Publisher
Nature Communications
Published On
Mar 12, 2024
Authors
Ziyuan Che, Xiao Wan, Jing Xu, Chrystal Duan, Tianqi Zheng, Jun Chen
Tags
wearable technology
voice synthesis
machine learning
vocal fold dysfunction
assistive technology
self-powered systems
magnetoelasticity

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