ArticleIEEE journal of translational engineering in health and medicine2019
Laryngeal Pressure Estimation With a Recurrent Neural Network.
Article in IEEE journal of translational engineering in health and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
- 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
- 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
- Three-Dimensional Analysis of Vocal Fold Oscillations: Correlating Superior and Medial Surface Dynamics Using Ex Vivo Human Hemilarynges.Bioengineering (Basel, Switzerland) · 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
- Voice Feature Selection to Improve Performance of Machine Learning Models for Voice Production Inversion.Journal of voice : official journal of the Voice Foundation · 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
- 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
- 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.Frontiers in physiology · 2021Article
- Estimation of vocal fold physiology from voice acoustics using machine learning.The Journal of the Acoustical Society of America · 2020Article
- The effect of high-speed videoendoscopy configuration on reduced-order model parameter estimates by Bayesian inference.The Journal of the Acoustical Society of America · 2019Article
- Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model.Applied sciences (Basel, Switzerland) · 2019Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantifying the physical parameters of voice production is essential for understanding the process of phonation and can aid in voice research and diagnosis. As an alternative to invasive measurements, they can be estimated by formulating an inverse problem using a numerical forward model. However, high-fidelity numerical models are often computationally too expensive for this. This paper presents a novel approach to train a long short-term memory network to estimate the subglottal pressure in the larynx at massively reduced computational cost using solely synthetic training data. We train the network on synthetic data from a numerical two-mass model and validate it on experimental data from 288 high-speed
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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.