Studying the Laryngeal Mechanisms Underlying Dysphonia in Connected Speech

研究连语语音中发声困难的喉部机制

• 批准号: 9901502
• 负责人: Maryam Naghibolhosseini
• 金额: $ 13.78万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9901502
• 关键词: Acoustics; Address; Age; Area; Auditory; Behavior; Biomechanics; Categories; Characteristics; Clinical; Communication; Coupling; Data; Data Set; Development; Diagnosis; Dysphonia; Endoscopes; Evaluation; Functional disorder; Goals; Gold; Health; Human; Image; Knowledge; Larynx; Lead; Machine Learning; Measurement; Measures; Methodology; Methods; Mining; Modeling; National Institute on Deafness and Other Communication Disorders; Operative Surgical Procedures; Outcome; Outcomes Research; Paralysed; Patients; Physics; Physiological; Prevention; Production; Protocols documentation; Research; Series; Severities; Source; Spastic Dysphonias; Speech; Speed; Statistical Data Interpretation; Statistical Models; Strategic Planning; System; Techniques; Testing; Therapeutic; Time; Tremor; Visual; Voice; Voice Disorders; Voice Disturbances; Voice Quality; base; clinical application; clinical development; clinical practice; clinically relevant; cohort; flexibility; image processing; imaging approach; improved; innovation; kinematics; sex; temporal measurement; time use; tool; treatment strategy; vibration; vocal cord; vocalization

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).

项目摘要/摘要 该提案旨在采用最近与高速耦合纤维内窥镜耦合的进步 视频注册镜检查（HSV）系统在连接的语音期间获得HSV记录。目标是学习声音 神经源性语音疾病患者的吞咽困难的机制。这条线的长期目标 研究是为了创建临床适用的定量方法，以测量声带振动的功能测量 在使用创新喉成像的连接语音中，一种可以推动临床声音的方法 评估和治疗实践。在AIM 1中，将开发基于HSV的人声折叠运动学测量 这些措施对患者语音音频感知质量的影响将得到确定。图像 将开发处理技术来从相互关联的语音中从HSV数据中提取此类度量。这 提取的措施将作为统计模型的输入，以确定差异的来源 在正常对照和患者之间的不同语音语音环境和单词之间。这个目标提供 一种基于HSV的无偏见方法，可预测语音质量。开发基于HSV的功能方法 连接语音中的喉检查可以增强临床语音评估。另外，更好 了解语音环境的影响将导致优化功能语音的协议 通过喉部成像进行评估。在AIM 2中，机器学习方法将是 被用来发现隐藏的物理学和鼻音中的喉音生产的未知喉机制 患者。该项目的发现将有助于对生物力学或生理学进行必要的调整 声带的特征以增强神经源性语音障碍患者的语音质量。因此， 这项研究的结果将帮助临床医生正确选择并制定新的治疗策略 （治疗，药用或外科手术），这是基于对喉部机制的知识 烦躁不安。拟议的研究与NIDCD的多个优先领域和谐相处， 2017-2021战略计划。两种目标都支持优先级3（改善诊断，治疗方法和 预防）通过制定基于HSV的目标措施并预测语音质量。比较 正常和无序的声音中的喉机制解决了优先级1（加深了我们对 人类交流系统的正常功能）。两种目的都建议研究喉机制 神经源性和功能性语音疾病的患者解决优先级2（增加我们的知识 关于改变或减少沟通和健康的条件）。