Towards Precision Assessment of Dysphonic Speech: From Vocal Fold Physiology to Perception

实现语音障碍言语的精确评估：从声带生理学到感知

• 批准号: 10364961
• 负责人: DIMITAR D DELIYSKI
• 金额: $ 63.73万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364961
• 关键词: Acoustics; Air Movements; Area; Articulation; Auditory; Auditory Perception; Behavior; Biomechanics; Categories; Clinic; Clinical; Complex; Computer Models; Data; Diagnostic; Dimensions; Dysphonia; Evaluation; Functional disorder; Goals; Image; Imaging technology; Individual; Investigation; Knowledge; Larynx; Lead; Left; Link; Machine Learning; Measurement; Measures; Methodology; Methods; NIH Program Announcements; Pathology; Pattern; Perception; Periodicity; Persons; Phase; Phonation; Physiological; Physiology; Positioning Attribute; Procedures; Protocols documentation; Psychoacoustics; Research; Research Personnel; Scientific Advances and Accomplishments; Severities; Speech; Speed; Techniques; Time; Translational Research; Treatment outcome; Variant; Voice; Voice Disorders; Voice Quality; clinical practice; improved; indexing; innovation; large datasets; machine learning model; multidisciplinary; neurophysiology; novel; phrases; predictive modeling; programs; tool; vibration; vocal cord; vocalization

Project Summary/Abstract The ways in which vocal fold (VF) vibratory behavior and resulting voice quality (VQ) perception differ across vowel categories and the co-articulatory variations of connected speech are unknown and largely unexplored. Furthermore, phonatory adjustments during connected speech (possible variations in voicing onsets and offsets and articulatory transitions) may provide important clinical information that can guide diagnostic protocols, may correspond closely with perceived handicap, and may represent functionally-relevant treatment targets. The overall goal of the proposed research is to discover and quantify physiological mechanisms underlying normal and abnormal VF behavior and establish their relationships to VQ perception in connected speech. This innovative proposal leverages the expertise of a multidisciplinary team and develops a comprehensive framework linking vocal physiology and perception with improved measurement approaches, methods, and analyses that account for the effects of co-articulation in connected speech. Abnormalities in pre-, post-, and peri-phonatory vibratory behavior are physiological hallmarks of voice disorders. Aim 1 will leverage precise, automated physiological measures of phonatory onset (pre-), offset (post-), and variation in VF phase asymmetry (peri-) to characterize VF vibratory behavior in uniform vowel- consonant-vowel (VCV) utterances with a controlled phonetic context. Aim 2 will establish relationship between these physiological measures and dimension-specific VQ perception in VCV utterances, using ratio- level matching tasks with physical units (e.g., dB) and biologically inspired computational models grounded in psychoacoustics and auditory-perception. These evaluative methods overcome technical and methodological limitations of conventional perceptual and acoustic methods. Aim 3 will evaluate and validate the three physiological measures in connected speech and discover new physiological signatures currently unknown through the use of novel and powerful machine-learning models that include as inputs physiological measures derived from high-speed videoendoscopy. Aim 4 will use automated, efficient dimension-specific computational models to evaluate VQ in connected speech and to discover physiological signatures that are related to VQ perception through machine learning. The unique combination of machine learning with computational models of VQ perception that are specific to VQ dimensions, rather than just overall severity, can effectively deal with the massive data associated with connected speech and high-speed videoendoscopy. Knowledge gained from this pre-translational research has the potential to improve our understanding of voice pathology and to substantially advance functional assessment and treatment outcomes for millions of people with hypo- and hyper-adductory voice disorders.

项目概要/摘要 声带 (VF) 振动行为和由此产生的语音质量 (VQ) 感知的方式存在差异 元音类别和相连语音的协同发音变化是未知的，并且很大程度上未经探索。 此外，连接语音期间的语音调整（发声开始和发声的可能变化） 偏移和发音过渡）可以提供重要的临床信息，可以指导诊断 协议，可能与感知到的障碍密切对应，并且可能代表功能相关 治疗目标。拟议研究的总体目标是发现并量化生理学 正常和异常 VF 行为的机制，并建立它们与 VQ 感知的关系 在连贯的言语中。这一创新提案利用了多学科团队的专业知识， 开发了一个综合框架，将声音生理学和感知与改进的测量联系起来 解释互联语音中协同发音效果的途径、方法和分析。 发声前、发声后和发声周围振动行为的异常是声音的生理标志 失调。目标 1 将利用发声开始（前）、抵消的精确、自动化的生理测量 （后）和 VF 相位不对称性的变化（周）来表征均匀元音中的 VF 振动行为 具有受控语音上下文的辅音-元音 (VCV) 话语。目标2将建立关系 在这些生理测量和 VCV 话语中特定维度的 VQ 感知之间，使用比率 具有物理单位（例如 dB）和基于生物启发的计算模型的水平匹配任务 在心理声学和听觉感知方面。这些评估方法克服了技术和 传统感知和声学方法的方法局限性。目标 3 将评估和验证 互联语音中的三种生理测量并发现当前新的生理特征 通过使用新颖且强大的机器学习模型（包括生理输入）来未知 源自高速视频内窥镜检查的措施。目标 4 将使用自动化、高效的特定维度 评估互联语音中的 VQ 并发现生理特征的计算模型 通过机器学习与 VQ 感知相关。机器学习与机器学习的独特结合 VQ 感知的计算模型特定于 VQ 维度，而不仅仅是整体严重性， 可以有效处理与互联语音和高速视频内窥镜相关的海量数据。 从这项预翻译研究中获得的知识有可能提高我们对语音的理解 病理学并大幅推进数百万人的功能评估和治疗结果 患有内收性声音障碍和内收性声音障碍。