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.

项目摘要/摘要 声带(VF)振动行为和由此产生的语音质量(VQ)感知的方式不同 元音类别和连接语音的协同发音变化是未知的，也在很大程度上没有被探索。 此外，在连接的语音过程中的发音调整(发音起点和发音的可能变化 偏移量和关节转换)可以提供重要的临床信息 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和量化生理学 VF行为正常和异常的机制及其与VQ知觉的关系 在连通的演讲中。这一创新的建议利用了多学科团队的专业知识， 开发一个全面的框架，将声音生理学和知觉与改进的测量联系起来 解释连通言语中协同发音的影响的途径、方法和分析。 发声前、发声后和发声前后的异常振动行为是嗓音的生理特征 精神错乱。目标1将利用精确的、自动化的发音(Pre-Pre)、偏移的生理测量 (后)和VF相位不对称的变化(PERI-)来表征一致元音的VF振动行为-- 具有受控语音语境的辅音-元音(VCV)发音。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知觉之间，使用比率- 物理单元(例如，分贝)和生物启发计算模型的水平匹配任务 在心理声学和听觉方面。这些评估方法克服了技术和 传统知觉和声学方法的方法论局限性。AIM 3将评估和验证 连通语音中的三种生理指标及目前发现的新的生理特征 通过使用新的和强大的机器学习模型而未知，该模型包括作为输入的生理 来自高速视频内窥镜检查的措施。AIM 4将使用自动化、高效的特定维度 评估连接语音中的VQ并发现符合以下条件的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 VQ知觉的计算模型是特定于VQ维度的，而不仅仅是总体严重性， 可以有效地处理与联网语音和高速视频内窥镜相关的海量数据。 从翻译前研究中获得的知识有可能提高我们对声音的理解 并大幅提高数百万人的功能评估和治疗结果 有低内收音和高内收音障碍。