ArticleFrontiers in physiology2024
Neural network-based estimation of biomechanical vocal fold parameters.
Article in Frontiers in physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Machine learning surrogate forward models for biomechanical laryngeal control.The Journal of the Acoustical Society of America · 2026Article
- Machine learning surrogate forward models for biomechanical laryngeal control.bioRxiv : the preprint server for biology · 2026Article
- Toward ambulatory monitoring of vocal behavior at the physiological level using deep ensembles and Bayesian neural networks.JASA express letters · 2025Article
- Estimation of Physiological Vocal Features from Neck Surface Acceleration Signals Using Probabilistic Bayesian Neural Networks.IEEE transactions on audio, speech, and language processing (2025) · 2025Article
- Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.Frontiers in artificial intelligence · 2025Article
- Floating Ball Voice Therapy: Preliminary Effects on Outcomes and Predicting Individual Patient Differences in Generalization.Journal of speech, language, and hearing research : JSLHR · 2024Article
- Neural network-based estimation of biomechanical vocal fold parameters.Frontiers in physiology · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vocal fold (VF) vibrations are the primary source of human phonation. High-speed video (HSV) endoscopy enables the computation of descriptive VF parameters for assessment of physiological properties of laryngeal dynamics, i.e., the vibration of the VFs. However, underlying biomechanical factors responsible for physiological and disordered VF vibrations cannot be accessed. In contrast, physically based numerical VF models reveal insights into the organ's oscillations, which remain inaccessible through endoscopy. To estimate biomechanical properties, previous research has fitted subglottal pressure-driven mass-spring-damper systems, as inverse problem to the HSV-recorded VF trajectories, by global optimization of the numerical model. A neural network trained on the numerical model may be used as a substitute for computationally expensive optimization, yielding a fast evaluating surrogate of the biomechanical inverse problem. This paper proposes a convolutional recurrent neural network (CRNN)-based architecture trained on regression of a physiological-based biomechanical six-mass model (6 MM). To compare with previous research, the underlying biomechanical factor "subglottal pressure" prediction was tested against 288 HSV
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.