# Bayesian Data-Driven Subject-Specific Modeling of Voice Production

> **NIH NIH R21** · MICHIGAN STATE UNIVERSITY · 2024 · $68,924

## Abstract

Project Summary/Abstract
 This proposal aims to develop Bayesian subject-specific computational models of voice production in vocally
normal individuals and patients with structural voice disorders. Voice production is a complex biophysical
process, consisting of vocal fold biomechanics and sub-glottal, intra-glottal, and supra-glottal aerodynamics, as
well as their interactions. Predictive computational modeling approaches are highly needed as they provide
scientific tools for better understanding the detailed function of such a sophisticated coupled system. They can
be employed to study the normal function of voice production and investigate how it can be impacted due to an
anomaly or malfunction in the vocal fold structure or behavior. Experimental data of high-speed videoendoscopy,
electroglottography and acoustic signals will be used to design computational models of voice production,
coupling laryngeal dynamics and aerodynamics. In Aim 1, the objective is to develop Bayesian predictive models
that can capture the uncertainties inherent in the data and models. The Bayesian inference will be performed
using the high-speed videoendoscopy and electroglottography data. The models will be validated with acoustic
signals for each vocally normal participant. The model will couple the vocal fold tissue vibration (kinetics and
kinematics) with the instantaneously interacting aerodynamics of glottal airflow to take into account the flow-
structure interaction during phonation. In Aim 2, the goal is to design patient-specific computational models of
voice production for patients with structural voice pathologies including vocal polyps, Reinke's edema, and
laryngitis. The assumption is that the vocal fold vibrations can be forced and fluid-induced in the patients. An
external patient-specific force component will be calculated from the model for the patients, where the physical
structure and vibratory behavior of the vocal folds are negatively impacted by the pathology. The parameter
uncertainties will be calculated and expected to vary greatly among the patients due to the disorders. The
outcome of this research will extend and deepen our understanding of the normal voice function and
pathophysiology of voice disorders. The proposed research is in harmony with multiple priority areas described
in the 2017-2021 Strategic Plan of the NIDCD [3]. Aim 1 supports Priority 1 (“deepen our understanding of the
normal function of the systems of human communication”) by designing computational models of voice
production for norm. Aim 2 proposes to determine vocal dynamics and glottal aerodynamics of voice production
in patients with structural voice disorders, which addresses Priority 2 (“increase our knowledge about conditions
that alter or diminish communication and health”). Both Aims support Priority 3 (“improve methods of diagnosis,
treatment, and prevention”) through determining what laryngeal mechanisms are disrupted in patients with voice
disorder and how...

## Key facts

- **NIH application ID:** 10952212
- **Project number:** 3R21DC020003-03S1
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Maryam Naghibolhosseini
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $68,924
- **Award type:** 3
- **Project period:** 2022-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10952212

## Citation

> US National Institutes of Health, RePORTER application 10952212, Bayesian Data-Driven Subject-Specific Modeling of Voice Production (3R21DC020003-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10952212. Licensed CC0.

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