Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech

项目概要/摘要 该提案旨在采用耦合光纤内窥镜的最新进展， 视频内窥镜（HSV）系统，以在连接的语音期间获得HSV记录。我们的目标是学习声乐 神经源性嗓音障碍患者发声困难的潜在机制这条线的长期目标是 研究的目的是建立临床上可应用的定量方法，用于声带振动的功能测量 在连接语音使用创新的喉部成像，一种方法，可以提高临床语音 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 并且将确定这些测量对患者的语音听觉质量的影响。图像 将开发处理技术以从连接语音中的HSV数据中提取这样的度量。的 提取的测量值将作为统计模型的输入，以确定差异的来源 正常人与患者在不同语音语境和词汇下的语音差异。这一目标提供了 一种基于HSV的无偏语音质量预测方法。开发这种基于HSV的功能性方法， 在连接语音中进行喉部检查可增强临床嗓音评估。此外，更好地 了解语音环境的影响将导致优化功能语音的协议 通过喉成像在连接语音评估。在目标2中，机器学习方法将被 用来发现隐藏的物理和未知的发声机制，在发声困难的 患者该项目的研究结果将有助于在生物力学或生理学方面进行必要的调整 声带的特征，以提高语音质量的患者与神经源性语音障碍。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），这是基于获得的知识喉机制， 发音困难拟议的研究与NIDCD的多个优先领域相协调， 2017 - 2021年战略计划。这两个目标都支持优先事项3（改进诊断、治疗和预防方法）。 预防）通过开发客观的HSV为基础的措施和预测语音质量。比较 喉机制在正常和紊乱的声音地址优先1（加深我们的理解， 人类通信系统的正常功能）。这两个目标都提出研究喉机制， 神经源性和功能性语音障碍患者，解决优先事项2（增加我们的知识 关于改变或减少沟通和健康的条件）。

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).