Article: Physics-informed modelling of vocal fold fatigue process using acoustic and laryngovibrography data Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.97 pp.89 - 118 Abstract: Vocal fold fatigue can cause organic lesions for a long time, and monitoring its evolution process is of great significance. The existing research mode single response is slow, static modelling is difficult to tolerate dynamic, poor interpretability and weak generalisation. This paper proposes physics-informed joint simulation and identification framework. The framework uses mathematical modelling of fatigue changes, simultaneous analysis of sound and vibration signals, and introduces the principle of sound to improve the authenticity of the signal, so as to realise the mutual optimisation of generation and recognition. Experimental results show that the signal simulation correlation coefficient of the framework is 0.92, the fatigue prediction determination coefficient is 0.86, and the F1 score is 0.89, which is significantly improved compared with the variational recurrent neural network. Inderscience Publishers - linking academia, business and industry through research

Title: Physics-informed modelling of vocal fold fatigue process using acoustic and laryngovibrography data

Authors: Xi Zhang; Ruixue Sun; Chunmeng Zhao; Yan Zhao

Addresses: Qinhuangdao Vocational and Technical College, Qinhuangdao, 066100, China ' Qinhuangdao Vocational and Technical College, Qinhuangdao, 066100, China ' Qinhuangdao Vocational and Technical College, Qinhuangdao, 066100, China ' Qinhuangdao Vocational and Technical College, Qinhuangdao, 066100, China

Abstract: Vocal fold fatigue can cause organic lesions for a long time, and monitoring its evolution process is of great significance. The existing research mode single response is slow, static modelling is difficult to tolerate dynamic, poor interpretability and weak generalisation. This paper proposes physics-informed joint simulation and identification framework. The framework uses mathematical modelling of fatigue changes, simultaneous analysis of sound and vibration signals, and introduces the principle of sound to improve the authenticity of the signal, so as to realise the mutual optimisation of generation and recognition. Experimental results show that the signal simulation correlation coefficient of the framework is 0.92, the fatigue prediction determination coefficient is 0.86, and the F1 score is 0.89, which is significantly improved compared with the variational recurrent neural network.

Keywords: vocal fold fatigue; timing simulation; multi-modal fusion; physical information deep learning; generative adversarial network.

DOI: 10.1504/IJICT.2026.156198

International Journal of Information and Communication Technology, 2026 Vol.27 No.97, pp.89 - 118

Received: 03 Jun 2026
Accepted: 06 Jul 2026
Published online: 07 Sep 2026 *

Title: Physics-informed modelling of vocal fold fatigue process using acoustic and laryngovibrography data

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