Advancing Voice Health: High-Fidelity Modeling of Human Voice Production from Neuromuscular Control to Acoustic Resonance

ORAL · Invited

Abstract

The human voice produces a remarkably diverse array of sounds, making it the primary channel for human communication and a potential interface for human-computer interaction. Voice production starts with neural stimulation of the intrinsic laryngeal muscles, which control the position and biomechanics of the vocal folds. Air from the lungs interacts with the vocal folds, generating self-sustained vibrations and creating a pulsatile jet in the larynx. These glottal air pulses form the primary sound sources, which pass through the supraglottal vocal tract, reshaping the sound spectrum as an acoustic resonator. This intricate process requires precise neuromuscular control of complex interactions among glottal flow dynamics, vocal fold vibrations, and vocal tract acoustics.

Developing a comprehensive model of voice production remains a significant challenge, limiting our ability to accurately analyze and simulate voice sounds for medical and technological applications. This talk presents a cutting-edge research endeavor focused on creating a high-fidelity computer model to simulate the entire voice production process, from laryngeal muscle contraction to flow-structure interactions, integrating realistic laryngeal anatomy and physiologically quantifiable inputs. The model has been used to investigate the cause-effect relationships between muscle contraction, glottal aerodynamics, vocal fold vibrations, and resulting voice acoustics. By capturing these intricate dynamics, the model provides critical insights into voice production mechanisms. Additionally, this work represents a significant step towards developing a patient-specific voice simulator, offering personalized simulation-guided voice management, training, and surgical planning.

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Publication: 1. Jiang, W., Zheng, X., and Xue, Q. (2024) "A computational study of the influence of thyroarytenoid and cricothyroid muscle interaction on vocal fold dynamics in an MRI based human laryngeal model", Biomechanics and Modeling in Mechanobiology, in press, DOI:10.1007/s10237-024-01869-9
2. Movahhedi, M., Liu, X-Y, Geng, B., Elemans, C., Xue, Q., Wang, J-X., Zheng, X. (2023). "Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks," Communications Biology, 6(1), 541.
3. Wang, X, Zheng, X., Xue Q. (2023). "The influence of fiber orientation of the conus elastics in vocal fold modeling," J. Biomech. Eng., 145(9), BIO-22-1167.
4. Zhang, Y., Jiang, W., Sun, L., Wang, J., Zheng, X., Xue, Q. (2022). "A deep-leaning based generalized empirical flow model of glottal flow during normal phonation," J. Biomech. Eng, 144(9), 091001.
5. Jiang, W., De Luzan, C. F., Wang, X., Oren, L., Khosla, S., Xue, Q. and Zheng, X. (2022). "Computational modeling of voice production using excised canine larynx," J. Biomech. Eng., 144(3), 021003.
6. Movahhedi, M., Geng, B., Xue, Q. and Zheng, X. (2021). "A computational framework for patient-specific surgical planning of type 1 thyroplasty," JASA-EL,1(12), 125203.
7. Geng, B., Movahhedi, M., Xue, Q. and Zheng, X. (2021). "Vocal fold vibration mode change due to cricothyroid and thyroarytenoid muscle interaction in a three-dimensional model of canine larynx," J. Acoust. Soc. Am. 150(2), 1176-1187
8. Movahhedi, M., Geng, B., Xue, Q. and Zheng, X. (2021). "Effects of cricothyroid and thyroarytenoid interaction on voice control: muscle activity, vocal fold biomechanics, flow and acoustics," J. Acoust. Soc. Am. 150(1), 29-42
9. Bodaghi, D., Jiang, W., Xue, Q., and Zheng, X. (2021). "Effects of supraglottal acoustics on fluid-structure interaction during human voice production," J. Biomech. Eng., 143(4): 041010.
10. Zhang, Y., Zheng, X. and Xue, Q. (2020). "A deep neural network based glottal flow model for predicting fluid-structure interaction during voice production," Applied Science, 10(2), 705.
11. Geng, B., Pham, N., Xue, Q. and Zheng, X. (2020). "A three-dimensional vocal fold posturing model based on muscle mechanics and magnetic resonance imaging of a canine larynx," J. Acoust. Soc. Am. 147(4), 2597-2608.
12. Jiang, W., Zheng, X. and Xue, Q. (2019). "Influence of vocal fold cover layer thickness on its vibratory dynamics during voice production," J. Acoust. Soc. Am. 146(1), 369-380.
13. Wang, X., Jiang, W., Xue, Q. and Zheng, X. (2021). "A computational study of the effects of vocal fold stiffness parameters on voice production," J. Voice 35(2), 327.
14. Pham, N., Xue, Q. and Zheng, X. (2018). "Coupling between a fiber-reinforced model and a Hill-based contractile model for passive and active tissue properties of laryngeal muscles: A finite element study," JASA-EL, 144(3), 248-253.
15. Jiang, W., Zheng, X., and Xue, Q. (2018). "Effect of longitudinal variation of vocal fold inner layer thickness on flow-structure interaction during phonation," J. Biomech Eng., 140(12), 121008.
16. Geng. B., Xue, Q. and Zheng, X. (2017). "A Finite Element Study on the Cause of Vocal Fold Vertical Stiffness Variation," JASA-EL, 141(4), 351-356.
17. Jiang, W., Zheng, X. and Xue, Q. (2017). "Computational Modeling of Fluid–Structure–Acoustics Interaction during Voice Production," Frontiers in Bioengineering and Biotechnology. 5(7).
18. Geng, B., Xue, Q. and Zheng, X. (2016). "The Effect of Vocal Fold Vertical Stiffness Variation on Voice Production," J. Acoust. Soc. Am. 140(4), 2856-2866.

Presenters

  • Qian Xue

    Rochester Institute of Technology

Authors

  • Qian Xue

    Rochester Institute of Technology