## 6 Conclusions  
To date, the approaches employed for developing subject-specific numerical VF models have focused on lumped-elements for the fitting model in the inverse analysis; as such, parameter estimates are often greatly abstracted from the physical tissue properties. To overcome these limitations, the present work proposes a FE model of the VFs for a fitting model. The FE model captures the geometry and layered structure of the VFs more accurately, treating them as a multi-layered viscoelastic body, thus better approximating their kinematics. Since the FE model directly employs the tissue properties, such as [PERSON]'s moduli, these properties are estimated directly. Estimation of material properties was demonstrated using HSV data of silicone VFs as the observation, showing good agreement between the estimated and \"ground truth\" material properties. The robustness of the method was demonstrated by considering experimental data with different degrees of medial compression and differing subglottal pressures. The FE model faithfully recovered the material properties in all cases, including the degree of medial compression, which was embedded into the FE model in the form of base displacement. This suggests that the employed Bayesian framework using a FE fitting model is sufficiently sensitive to distinguish between different experimental conditions, even though the model was restricted to two dimensions. The FE models were validated by comparing the volumetric flow rate predicted by the model with experimentally measured values. This observation was not included in the estimation process and, as such, was an independent measure. The volumetric flow rate was slightly over-predicted, but generally agreed well. The exception was the highest subglottal pressure case, which was considerably over-predicted. Additionally, the contact pressures extracted from the developed FE models were found to increase with increasing subglottal pressure and medial compression, which is a trend that qualitatively agrees with previous studies. The highest subglottal pressure case was again an outlier, suggesting that the FE model for that case does not accurately capture the kinematics of the silicone VFs, likely due to the 2D geometry. This could potentially be improved by incorporating additional observations in the estimation procedure or expanding to a three-dimensional model. The stability of the results was examined with respect to numerical parameters, such as the importance sampling ensemble size, time step size, and mesh density. Estimated values converged at relatively modest ensemble sizes, though resolving the uncertainties required considerably more samples. Decreasing the time step size and increasing the mesh density lead to smaller uncertainties at the cost of significant computational time. One of the main drawbacks to our proposed model is the computational complexity; this cost will increase if more complex fluids models, a three-dimensional geometry, or acoustics are included. One source of uncertainty that has not been considered in this work is the structure of the layers. All estimates in this work have been computed with a FE model that was formed treating the layers and dimensions of the silicone VFs as perfectly known. The use of imperfect layers will affect the estimates and uncertainties of the material properties, however, this is the subject of ongoing research and requires a further examination. As an introductory effort, incorporating a FE fitting model into the Bayesian estimation framework has shown good promise. Future work includes validating the contact pressure estimates with experimental data, implementing a three-dimensional FE model, and employing clinical HSV.