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

项目概要/摘要 声带（VF）振动行为和由此产生的语音质量（VQ）感知在不同的 元音类别和连接语音的协同发音变化是未知的，并且在很大程度上未被探索。 此外，在连接语音期间的发声调整（发声起始和发声起始的可能变化）也是可能的。 偏移和发音转变）可以提供重要的临床信息， 协议，可能与感知的障碍密切相关，并可能代表功能相关 治疗目标。拟议研究的总体目标是发现和量化生理 正常和异常VF行为的潜在机制，并建立它们与VQ感知的关系 在连接的讲话。这一创新提案利用了多学科团队的专业知识， 发展了一个综合框架，将声音生理学和感知与改进的测量联系起来 的途径，方法和分析，帐户的影响，在连接的语音。 发声前、发声后和发声周振动行为的异常是嗓音的生理特征 紊乱目标1将利用精确的，自动化的生理测量发声起始（前）， （后）和VF相位不对称性（后）的变化来表征均匀元音中的VF振动行为。 辅音-元音（VCV）话语与受控的语音上下文。目标2建立关系 使用比率，在这些生理测量和VCV话语中特定维度的VQ感知之间进行比较 将任务与物理单元进行级别匹配（例如，dB）和生物启发的计算模型接地 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学局限性。目标3将评估和验证 研究了连接语音中的三种生理特征，并发现了新的生理特征 通过使用新的和强大的机器学习模型，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将使用自动化的、高效的特定维度 计算模型来评估连接语音中的VQ，并发现 与VQ感知相关的机器学习。机器学习与 VQ感知的计算模型是特定于VQ维度的，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语音的理解 病理学，并大大推进数百万人的功能评估和治疗结果 内收性和内收性嗓音障碍的患者

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.