ArticleThe Journal of the Acoustical Society of America2020
Estimation of vocal fold physiology from voice acoustics using machine learning.
Article in The Journal of the Acoustical Society of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in voice analysis for diagnosing vocal cord pathologies: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Pooled it
- 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
- Perceiving Physiology from the Voice: Evidence for Physiological Coupling Between Laryngeal and Epilaryngeal Adjustments.Journal of voice : official journal of the Voice Foundation · 2026Article
- Toward ambulatory monitoring of vocal behavior at the physiological level using deep ensembles and Bayesian neural networks.JASA express letters · 2025Article
- Subject-Specific Modeling by Domain Adaptation for the Estimation of Subglottal Pressure from Neck-Surface Acceleration Signals.Biomedical signal processing and control · 2025Article
- Early fiber development in human vocal folds: An in vitro pilot study.JASA express letters · 2025Article
- Characterization of upper esophageal sphincter pressures relative to vocal acoustics.Journal of applied physiology (Bethesda, Md. : 1985) · 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
- An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Review
- Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting.Journal of speech, language, and hearing research : JSLHR · 2024Article
- Neural network-based estimation of biomechanical vocal fold parameters.Frontiers in physiology · 2024Article
- Deep Learning for Neuromuscular Control of Vocal Source for Voice Production.Applied sciences (Basel, Switzerland) · 2024Article
- Voice Feature Selection to Improve Performance of Machine Learning Models for Voice Production Inversion.Journal of voice : official journal of the Voice Foundation · 2023Article
- 3D VOSNet: Segmentation of endoscopic images of the larynx with subsequent generation of indicators.Heliyon · 2023Article
- Estimating subglottal pressure and vocal fold adduction from the produced voice in a single-subject study (L).The Journal of the Acoustical Society of America · 2022Article
- Contribution of laryngeal size to differences between male and female voice production.The Journal of the Acoustical Society of America · 2021Article
- The influence of flow model selection on finite element model parameter estimation using Bayesian inference.JASA express letters · 2021Article
- A one-dimensional flow model enhanced by machine learning for simulation of vocal fold vibration.The Journal of the Acoustical Society of America · 2021Article
- 3D-FV-FE Aeroacoustic Larynx Model for Investigation of Functional Based Voice Disorders.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
The goal of this study is to estimate vocal fold geometry, stiffness, position, and subglottal pressure from voice acoustics, toward clinical and other voice technology applications. Unlike previous voice inversion research that often uses lumped-element models of phonation, this study explores the feasibility of voice inversion using data generated from a three-dimensional voice production model. Neural networks are trained to estimate vocal fold properties and subglottal pressure from voice features extracted from the simulation data. Results show reasonably good estimation accuracy, particularly for vocal fold properties with a consistent global effect on voice production, and reasonable agreement with excised human larynx experiment.
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Registered trials
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.