Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech

项目摘要/摘要 这项建议旨在利用高速耦合光纤内窥镜的最新进展 视频内窥镜(HSV)系统，用于在连接的语音过程中获取HSV录音。我们的目标是学习声乐 神经性发声障碍患者发音困难的机制。这条线路的长期目标是 研究旨在为声带振动的功能测量创造临床适用的定量方法。 在使用创新喉部成像的连接语音中，一种可以提高临床语音的方法 评估和治疗实践。在目标1中，将开发基于HSV的声带运动学测量 并将确定这些措施对患者声音听觉感知质量的影响。图像 将开发处理技术来从连接语音中的HSV数据中提取这样的测量。这个 提取的测量值将作为统计模型的输入，以确定差异的来源 正常对照组和患者对不同的语音、语音环境和词汇的反应。这个目标提供了 一种基于HSV的无偏语音质量预测方法。开发基于HSV的函数式方法 连通语音中的喉部检查可提高临床嗓音评估水平。此外，更好的 了解语音语境的影响将有助于优化功能语音的协议 通过喉部成像对连通语音的评估。在目标2中，机器学习方法将是 被用来发现发声困难患者发声的隐藏物理和未知的喉部机制 病人。该项目的研究结果将有助于在生物力学或生理方面进行必要的调整。 神经性嗓音障碍患者声带特征对提高嗓音质量的作用。因此， 这项研究的结果将有助于临床医生正确选择和开发新的治疗策略 (治疗的、医学的或外科的)，这是基于对喉部机制的获得的知识 发音困难。拟议的研究与NIDCD的多个优先领域是一致的，如 2017-2021年战略规划。这两个目标都支持优先事项3(改进诊断、治疗和 预防)通过制定基于HSV的客观措施和预测语音质量。比较 正常和紊乱声音的喉部机制解决了优先事项1(加深我们对 人类通信系统的正常功能)。这两个目标都提出了研究喉部机制的建议 神经源性和功能性发音障碍患者，这涉及到优先事项2(增加我们的知识 关于改变或削弱沟通和健康的条件)。

Project Summary/Abstract This proposal aims to employ the recent advancement of coupling fiberoptic endoscopes with high-speed videoendoscopy (HSV) systems to obtain HSV recordings during connected speech. The goal is to study vocal mechanisms underlying dysphonia in patients with neurogenic voice disorders. The long-term goal of this line of research is to create clinically applicable quantitative methods for functional measurement of vocal fold vibration in connected speech using innovative laryngeal imaging, an approach that could advance clinical voice assessment and treatment practice. In Aim 1, HSV-based measures of vocal fold kinematics will be developed and the influence of these measures on voice audio-perceptual qualities in the patients will be determined. Image processing techniques will be developed to extract such measures from the HSV data in connected speech. The extracted measures will be given as inputs to the statistical models to determine the source of the differences between the normal controls and the patients for different speech phonetic contexts and words. This aim provides an unbiased HSV-based method to predict voice quality. Developing such HSV-based methodology for functional laryngeal examination in connected speech can enhance clinical voice assessment. In addition, better understanding the influence of phonetic context would lead to optimizing the protocols for functional voice assessment through laryngeal imaging in connected speech. In Aim 2, machine learning approaches will be employed to discover hidden physics and unknown laryngeal mechanisms of voice production in the dysphonic patients. The findings of this project will help make necessary adjustments in biomechanical or physiological characteristics of vocal folds to enhance voice quality in patients with neurogenic voice disorders. Therefore, the outcome of this research will aid clinicians in properly selecting, and developing new treatment strategies (therapeutic, medicinal, or surgical), which are based on the gained knowledge of laryngeal mechanisms of dysphonia. The proposed research is in harmony with multiple priority areas of the NIDCD, described in the 2017-2021 Strategic Plan. Both aims support Priority 3 (improve methods of diagnosis, treatment, and prevention) through developing objective HSV-based measures and predicting the voice quality. Comparing laryngeal mechanisms in normal and disordered voices addresses Priority 1 (deepen our understanding of the normal function of the systems of human communication). Both aims propose to study laryngeal mechanisms in patients with neurogenic and functional voice disorders, which addresses Priority 2 (increase our knowledge about conditions that alter or diminish communication and health).