## 1 Introduction
used computed tomography to reconstruct the larynx of a subject in a computational domain. The external geometry of the VFs was obtained from the scan, whereas the VF layers and material properties were assumed from population-based histological data. [PERSON] et al. constructed a laryngeal model of a rabbit from magnetic resonance images. Two VF layers were observed in the scans, leading to a body-cover-type construction wherein the densities and Poisson ratios were assumed a priori. The elastic moduli of the layers were determined by an informal optimization that attempted to match the maximum glottal width and fundamental frequency of the model with HSV measurements. In contrast to optimization-based and machine learning methods that treat estimated model parameters as deterministic, the Bayesian framework treats all parameters and measurements as random variables. Such a framework has the benefit of elucidating the propagation of measurement uncertainties to the model parameters and outputs. Uncertainty estimates are powerful as they provide additional information about the fidelity of the model outputs, which are expected to be of value for clinical decisions. [PERSON] et al. [10] introduced the Bayesian framework to estimate stationary (non-time-varying) parameters of a two mass VF model using an importance sampling approach. This work was extended to estimate non-stationary parameters of a three mass body-cover model using both a particle filter [11] and an extended Kalman filter [21]. The Bayesian framework has, thus far, been demonstrated using simulated observation data from a reduced-order VF model, wherein the ground truth was known a priori, to estimate the parameters of a different reduced-order VF fitting model. The similarity between the generation and fitting models enabled direct assessment of the performance of the Bayesian framework. Herein we build on the Bayesian inference framework for developing subject-specific VF models by: (i) considering real HSV recordings of self-oscillating silicone VFs as the observation data; and, for the first time, (ii) employing a two-dimensional (2D) finite element (FE) model of the VFs as the fitting model. These two considerations address several limitations in prior Bayesian estimation-based inference studies applied to voiced speech, while simultaneously extending the methodology to more complex VF representations. Specifically, actual experimental data, with its inherent uncertainties, are used as the observations. Furthermore, by employing a FE representation of the VFs, the estimated FE parameters have direct relation to the material properties of the silicone VF models, reducing the layer of abstraction associated with reduced-order models. By using silicone VFs to generate the observation data, however, the \"ground truth\" is still available to assess the performance (estimated properties and their uncertainties) of the estimation procedure. Lastly, the use of experimental data provides a more realistic test bed due to the dynamical differences between the data source and the fitting model. We consider two different VF base configurations, with and without medial compression, as well as multiple subglottal pressures. Exploring medial compression offers insights into how FE models and Bayesian estimation can incorporate VF posturing for subject-specific models, while varying subglottal pressure affords a mechanism for evaluating how well Bayesian inference, when accounting for measurement uncertainty, can differentiate between phonation conditions. The paper is structured as follows: Section 2 discusses the silicone VFs and experimental conditions used during collection of the data sets and handling of the HSV. Respectively, Sections 3 and 4 briefly discuss the mathematical model and estimation procedure used in the analysis. Section 5 presents the results of the estimates and provides discussion of the findings.