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

Ziyuan Che, Xiao Wan, Jing Xu, Chrystal Duan, Tianqi Zheng and Jun Chen ()
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Ziyuan Che: University of California, Los Angeles
Xiao Wan: University of California, Los Angeles
Jing Xu: University of California, Los Angeles
Chrystal Duan: University of California, Los Angeles
Tianqi Zheng: University of California, Los Angeles
Jun Chen: University of California, Los Angeles

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract Voice disorders resulting from various pathological vocal fold conditions or postoperative recovery of laryngeal cancer surgeries, are common causes of dysphonia. Here, we present a self-powered wearable sensing-actuation system based on soft magnetoelasticity that enables assisted speaking without relying on the vocal folds. It holds a lightweighted mass of approximately 7.2 g, skin-alike modulus of 7.83 × 105 Pa, stability against skin perspiration, and a maximum stretchability of 164%. The wearable sensing component can effectively capture extrinsic laryngeal muscle movement and convert them into high-fidelity and analyzable electrical signals, which can be translated into speech signals with the assistance of machine learning algorithms with an accuracy of 94.68%. Then, with the wearable actuation component, the speech could be expressed as voice signals while circumventing vocal fold vibration. We expect this approach could facilitate the restoration of normal voice function and significantly enhance the quality of life for patients with dysfunctional vocal folds.

Date: 2024
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DOI: 10.1038/s41467-024-45915-7

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