ArticleFrontiers in physiology2021
Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 21 citations in OpenAlex.
- Machine learning surrogate forward models for biomechanical laryngeal control.The Journal of the Acoustical Society of America · 2026Article
- AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.Research square · 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
- Toward Acoustic-Based Normalization of Laryngeal EMG for Improved Interspeaker Consistency in Muscle-to-Acoustic Mapping.Journal of voice : official journal of the Voice Foundation · 2025Article
- EEG oscillations and related brain generators of phonation phases in long utterances.Scientific reports · 2025Article
- Subject-Specific Modeling by Domain Adaptation for the Estimation of Subglottal Pressure from Neck-Surface Acceleration Signals.Biomedical signal processing and control · 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
- 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 · 2024Article
- 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
- Ambulatory Monitoring of Subglottal Pressure Estimated from Neck-Surface Vibration in Individuals with and without Voice Disorders.Applied sciences (Basel, Switzerland) · 2022Article
- LaDIVA: A neurocomputational model providing laryngeal motor control for speech acquisition and production.PLoS computational biology · 2022Article
- 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
- Biomechanical mechanism of reduced aspiration by the Passy-Muir valve in tracheostomized patients following acquired brain injury: Evidences from subglottic pressure.Frontiers in neuroscience · 2022Article
- Kalman Filter Implementation of Subglottal Impedance-Based Inverse Filtering to Estimate Glottal Airflow during Phonation.Applied sciences (Basel, Switzerland) · 2022Article
- Triangular body-cover model of the vocal folds with coordinated activation of the five intrinsic laryngeal muscles.The Journal of the Acoustical Society of America · 2022Article
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Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
Abstract
The ambulatory assessment of vocal function can be significantly enhanced by having access to physiologically based features that describe underlying pathophysiological mechanisms in individuals with voice disorders. This type of enhancement can improve methods for the prevention, diagnosis, and treatment of behaviorally based voice disorders. Unfortunately, the direct measurement of important vocal features such as subglottal pressure, vocal fold collision pressure, and laryngeal muscle activation is impractical in laboratory and ambulatory settings. In this study, we introduce a method to estimate these features during phonation from a neck-surface vibration signal through a framework that integrates a physiologically relevant model of voice production and machine learning tools. The signal from a neck-surface accelerometer is first processed using subglottal impedance-based inverse filtering to yield an estimate of the unsteady glottal airflow. Seven aerodynamic and acoustic features are extracted from the neck surface accelerometer and an optional microphone signal. A neural network architecture is selected to provide a mapping between the seven input features and subglottal pressure, vocal fold collision pressure, and cricothyroid and thyroarytenoid muscle activation. This non-linear mapping is trained solely with 13,000 Monte Carlo simulations of a voice production model that utilizes a symmetric triangular body-cover model of the vocal folds. The performance of the method was compared against laboratory data from synchronous recordings of oral airflow, intraoral pressure, microphone, and neck-surface vibration in 79 vocally healthy female participants uttering consecutive /pæ/ syllable strings at comfortable, loud, and soft levels. The mean absolute error and root-mean-square error for estimating the mean subglottal pressure were 191 Pa (1.95 cm H
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