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共同发音变化尚不清楚，并且在很大程度上没有探索。 此外，连接语音期间的语音调整（在发声和发声和 偏移和关节过渡）可能会提供重要的临床信息，以指导诊断 协议可以与感知的障碍密切相对应，并且可以代表与功能相关的 治疗目标。拟议研究的总体目标是发现和量化生理 正常和异常VF行为的基础机制，并建立与VQ感知的关系 在连接的演讲中。该创新的建议利用了多学科团队的专业知识， 开发一个综合框架，将声音生理和感知与改进的测量联系起来 方法，方法和分析说明了连接语音中共同引起的影响。 前后，后和周围振动行为的异常是语音的生理标志 疾病。 AIM 1将利用语音发作（Pre-），偏移的精确，自动化的生理度量 （后）和VF期不对称（骨）的变化以表征均匀元音的VF振动行为 具有控制的语音上下文的辅音元音（VCV）话语。 AIM 2将建立关系 在VCV话语中这些生理措施和尺寸特异性VQ感知之间，使用比率 使用物理单元（例如DB）和以生物学启发的计​​算模型接地的级别匹配任务 在心理声学和听觉感知中。这些评估方法克服了技术和 常规感知和声学方法的方法学局限性。 AIM 3将评估和验证 相互关联的语音中的三种生理措施并发现了目前的新生理特征 通过使用新颖且功能强大的机器学习模型未知，该模型包括输入生理 源自高速视频镜检查的度量。 AIM 4将使用自动化的，有效的尺寸特异性 评估连接语音中VQ的计算模型并发现生理特征 通过机器学习与VQ感知有关。机器学习与 VQ感知的计算模型，特定于VQ维度，而不仅仅是整体严重性， 可以有效地处理与连接的语音和高速视频镜检查相关的大量数据。 从这项前翻译的研究中获得的知识有可能提高我们对声音的理解 病理学并大大提高数百万人的功能评估和治疗结果 患有低调和高调语音障碍。