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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ziyuan Che & Xiao Wan & Jing Xu & Chrystal Duan & Tianqi Zheng & Jun Chen, 2024. "Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45915-7
    DOI: 10.1038/s41467-024-45915-7
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    References listed on IDEAS

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    1. Lu-Qi Tao & He Tian & Ying Liu & Zhen-Yi Ju & Yu Pang & Yuan-Quan Chen & Dan-Yang Wang & Xiang-Guang Tian & Jun-Chao Yan & Ning-Qin Deng & Yi Yang & Tian-Ling Ren, 2017. "An intelligent artificial throat with sound-sensing ability based on laser induced graphene," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    2. Wei Yan & Grace Noel & Gabriel Loke & Elizabeth Meiklejohn & Tural Khudiyev & Juliette Marion & Guanchun Rui & Jinuan Lin & Juliana Cherston & Atharva Sahasrabudhe & Joao Wilbert & Irmandy Wicaksono &, 2022. "Single fibre enables acoustic fabrics via nanometre-scale vibrations," Nature, Nature, vol. 603(7902), pages 616-623, March.
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