Bayesian Data-Driven Subject-Specific Modeling of Voice Production

项目摘要/摘要 这项建议旨在开发特定于受试者的贝叶斯声乐发声计算模型。 正常人和结构性嗓音障碍患者。发声是一种复杂的生物物理过程 过程，包括声带生物力学和声门下、声门内和声门上空气动力学，AS 以及他们之间的互动。非常需要预测计算建模方法，因为它们提供了 更好地理解这种复杂耦合系统的详细功能的科学工具。他们可以 被用来研究发声的正常功能，并调查由于 声带异常声带结构或行为的异常或故障高速视频内窥镜的实验数据， 电声门图和声学信号将被用来设计语音产生的计算模型， 喉部动力学和空气动力学的耦合。在目标1，目标是开发贝叶斯预测模型 这可以捕获数据和模型中固有的不确定性。将执行贝叶斯推理 使用高速视频内窥镜和电声门图数据。这些模型将通过声学验证 每个声音正常的参与者的信号。该模型将声带组织的振动(动力学和 运动学)与声门气流的瞬时相互作用的空气动力学，以考虑流动- 发声过程中的结构相互作用。在目标2中，目标是设计特定于患者的计算模型 声带息肉、Reinke‘s水肿等结构性嗓音病变患者的发声 喉炎。假设患者的声带振动可以是强迫的和液体诱导的。一个 将根据患者的模型计算特定于患者的外部力分量，其中物理 声带的结构和振动行为受到病理的负面影响。该参数 不确定度将被计算出来，预计由于疾病的原因，患者之间的差异会很大。这个 这项研究的成果将扩大和加深我们对正常语音功能和 嗓音障碍的病理生理学。拟议的研究与所述的多个优先领域是一致的 在NIDCD的2017-2021年战略规划中[3]。目标1支持优先事项1(“加深我们对 人类交流系统的正常功能“)通过设计语音的计算模型 以生产为标准。目的2建议确定发声动力学和声门空气动力学的发声 在患有结构性发声障碍的患者中，这涉及到优先事项2(增加我们对疾病的了解 改变或削弱沟通和健康“)。这两个目标都支持优先3(“改进诊断方法， 治疗和预防“)通过确定嗓音患者的喉部机制被扰乱 以及它是如何影响声学信号的。

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 it affects the acoustic signal.