Quantifying articulatory performance in children with dysarthria: Development of an automated metric for clinical use

量化构音障碍儿童的发音能力：开发用于临床的自动化指标

• 批准号: 10439252
• 负责人: Visar Berisha
• 金额: $ 64.64万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10439252
• 关键词: Acoustics; Address; Age; Algorithms; Articulation; Assessment tool; Categories; Cerebral Palsy; Child; Childhood; Clinical; Clinical Research; Complement; Data; Development; Dysarthria; Ensure; Foundations; Goals; Grain; Growth; Individual; Intervention; Laboratory Study; Lead; Light; Loudness; Machine Learning; Measurement; Measures; Methods; Modeling; Outcome; Outcomes Research; Pattern; Performance; Phonation; Production; Research; Sampling; Specific qualifier value; Speech; Speech Intelligibility; Speech Pathology; Speech Sound; Speech Therapy; Statistical Models; System; Testing; Time; Translating; base; improved; individual variation; peer; sound; tool

Project Summary Half of children with cerebral palsy (CP) have dysarthria, which has well documented negative effects on speech intelligibility. A primary aim of treatment is improving speech intelligibility. Current pediatric dysarthria interventions focus on clear and/or loud speech, phonation, speech rate, or some combination of these. While several interventions have resulted in intelligibility gains, there is substantial variability in outcomes. Additional interventions are needed to ensure that intelligibility can be maximized for all children. Laboratory studies have consistently identified the articulatory system as the largest contributor to intelligibility deficits in dysarthria, implicating variables such as vowel space and F2 slope. However, these measures are not clinically accessible, nor do they translate to specific treatment targets. Given the primacy of the articulatory system to intelligibility and developmental malleability associated with the acquisition of speech in children, there is a need for sensitive metrics to quantify articulation using clinically meaningful units (i.e., speech sounds). Such metrics would enable us to understand how different speech sounds contribute to intelligibility and would lead to advancements in the development of data-driven interventions for improving intelligibility in children with dysarthria, complementing existing therapies. Our goal is to develop a clinical tool that yields objective, continuous, and automatic quantification of speech sound articulation from connected speech in children; we will use this tool to quantify the contributions of individual phonemes to intelligibility. To do this, we will use state-of-the art speech analytics involving machine learning for acoustic modeling, and clinical research in speech pathology to refine and validate algorithms that specify a phoneme log-likelihood ratio (PLLR) for each phoneme in English. We will use the PLLR to create growth curves for the development of phoneme articulation based on data from 750 typically developing children between the ages of 2 ½ and 10 years, and to characterize the contribution of individual phonemes to intelligibility by age in these children. We will then examine 700 longitudinal speech samples from children with dysarthria between the ages of 2 ½ and 10 years, and identify how they differ from typical children in phoneme development and how speech sound articulation contributes to intelligibility. The outcome of this research is an algorithm that can quantify phoneme articulation in children, indicating a child’s performance relative to norms for each phoneme. Results will specify the relative contribution of individual phonemes to intelligibility, and how this changes developmentally and in the context of dysarthria. A fine-grained understanding of the impact of dysarthria on phoneme development and subsequent contributions to intelligibility has never before been feasible and will have direct clinical and theoretical implications. Results will lay the foundation for new assessments and treatments for improving intelligibility in childhood dysarthria.

项目概要 一半的脑瘫 (CP) 儿童患有构音障碍，这已被充分证明对语言产生负面影响 言语清晰度。治疗的主要目的是改善当前的儿童构音障碍。 虽然注意力会干扰清晰和/或响亮的语音、发声、语速或这些的某些组合。 一些干预措施导致了清晰度的提高，但结果存在很大差异。 实验室研究表明，需要采取干预措施以确保最大限度地提高所有儿童的清晰度。 一致认为发音系统是构音障碍中清晰度缺陷的最大贡献者， 涉及元音空间和 F2 斜率等变量然而，这些测量在临床上无法实现， 鉴于发音系统对清晰度的首要作用，它们也不能转化为特定的治疗目标。 和与儿童言语习得相关的可塑性，需要 使用具有临床意义的单位（即语音）来量化发音的敏感指标。 将使我们能够理解不同的语音如何有助于清晰度，并导致 在开发数据驱动的干预措施以提高儿童智力方面取得的进展 构音障碍，补充现有的治疗方法，我们的目标是开发一种能够产生客观的临床工具。 我们对儿童的连接语音进行连续、自动的量化； 将使用此工具来量化各个手机对清晰度的贡献为此，我们将使用。 最先进的语音分析，涉及声学建模的机器学习和临床研究 语音病理学，以细化和验证为每个音素指定音素对数似然比 (PLLR) 的算法 我们将使用 PLLR 来创建音素发音发展的增长曲线。 基于 750 名 2.5 岁至 10 岁之间典型发育儿童的数据， 然后我们将按年龄来描述个人电话对这些儿童的清晰度的贡献。 检查 700 个 2.5 至 10 岁患有构音障碍儿童的纵向言语样本， 并确定他们在音素发展方面与典型儿童有何不同以及语音发音如何 这项研究的成果是一种可以量化音素清晰度的算法。 在儿童中，表明儿童相对于每个音素标准的表现 结果将指定相对值。 个人手机对清晰度的贡献，以及这种变化在发展过程中以及在 构音障碍对音素发育及其后续影响的详细了解。 对可懂度的贡献从来都是不可能的，并且将具有直接的临床和理论意义 结果将为提高可理解性的新评估和治疗奠定基础。 儿童构音障碍。