Cisonni Julien, Van Hirtum Annemie, Pelorson Xavier, Willems Jan
Department of Speech and Cognition, GIPSA-Laboratory, UMR CNRS 5216, Grenoble Universities, 961 rue de la Houille Blanche, BP 46, 38402 Saint Martin d'Heres, France.
J Acoust Soc Am. 2008 Jul;124(1):535-45. doi: 10.1121/1.2931959.
In physical modeling of phonation, the pressure drop along the glottal constriction is classically assessed with the glottal geometry and the subglottal pressure as known input parameters. Application of physical modeling to study phonation abnormalities and pathologies requires input parameters related to in vivo measurable quantities commonly corresponding to the physical model output parameters. Therefore, the current research presents the inversion of some popular simplified flow models in order to estimate the subglottal pressure, the glottal constriction area, or the separation coefficient inherent to the simplified flow modeling for steady and unsteady flow conditions. The inverse models are firstly validated against direct simulations and secondly against in vitro measurements performed for different configurations of rigid vocal fold replicas mounted in a suitable experimental setup. The influence of the pressure corrections related to viscosity and flow unsteadiness on the flow modeling is quantified. The inversion of one-dimensional glottal flow models including the major viscous effects can predict the main flow quantities with respect to the in vitro measurements. However, the inverse model accuracy is strongly dependent on the pertinence of the direct flow modeling. The choice of the separation coefficient is preponderant to obtain pressure predictions relevant to the experimental data.
在发声的物理建模中，沿声门收缩处的压降传统上是通过已知的输入参数——声门几何形状和声门下压力来评估的。将物理建模应用于研究发声异常和病理情况需要与体内可测量量相关的输入参数，这些参数通常与物理模型的输出参数相对应。因此，当前的研究提出对一些流行的简化流动模型进行反演，以估计声门下压力、声门收缩面积或简化流动建模中固有的分离系数，用于稳定和不稳定流动条件。首先，将反演模型与直接模拟进行对比验证，其次与针对安装在合适实验装置中的不同配置刚性声带复制品进行的体外测量进行对比验证。量化了与粘性和流动不稳定性相关的压力校正对流动建模的影响。包括主要粘性效应的一维声门流动模型的反演可以预测相对于体外测量的主要流动量。然而，反演模型的准确性强烈依赖于直接流动建模的相关性。分离系数的选择对于获得与实验数据相关的压力预测至关重要。
