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: 10601094
• 负责人: Visar Berisha
• 金额: $ 61.72万
• 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/10601094
• 关键词: Acoustics; Address; Age; Algorithms; Articulation; Assessment tool; Cerebral Palsy; Child; Childhood; Clinical; Clinical Research; Complement; Data; Development; Dysarthria; Foundations; Goals; Grain; Growth; Individual; Intervention; Laboratory Study; 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; 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.

项目摘要 一半的脑性瘫痪(CP)儿童有构音障碍，这对儿童的负面影响有很好的证据 语音清晰度。治疗的一个主要目的是提高语音清晰度。当前儿童构音障碍 干预的重点是清晰和/或响亮的语音、发音、语速或这些因素的某种组合。而当 虽然有几项干预措施提高了可理解性，但结果存在很大的差异。其他内容 需要采取干预措施，以确保最大限度地提高所有儿童的可理解性。实验室研究表明 一致认为发音系统是构音障碍可理解性缺陷的最大贡献者， 隐含变量，如元音间距和F2斜率。然而，这些措施在临床上是不可获得的， 它们也不会转化为具体的治疗目标。鉴于发音系统对可理解性的重要性 和与儿童语言习得相关的发育延展性，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。这样的指标 将使我们能够理解不同的语音如何有助于清晰度，并将导致 以数据为导向的干预措施在改善儿童可理解性方面的进展 构音障碍，补充现有的治疗方法。我们的目标是开发一种临床工具，能够产生客观的、 从儿童的连通语音中连续、自动量化语音清晰度；我们 将使用这一工具来量化单个音素对清晰度的贡献。为此，我们将使用 最先进的语音分析，包括用于声学建模的机器学习和临床研究 语音病理，以改进和验证为每个语音指定音素对数似然比(PLLR)的算法 英语中的音素。我们将使用PLLR来创建音素发音发展的增长曲线 基于750名年龄在2岁半到10岁之间的典型发育儿童的数据， 根据这些儿童的年龄，描述单个音素对清晰度的贡献。到时候我们会的 研究了700个来自2岁半至10岁构音障碍儿童的纵向语音样本， 并找出他们在音素发育和语音清晰度方面与普通儿童的不同之处 有助于提高清晰度。这项研究的结果是一种可以量化音素发音的算法 在儿童中，表示儿童相对于每个音素的正常表现。结果将指定相对的 单个音素对清晰度的贡献，以及这种贡献如何在发展中和在 构音障碍。对构音障碍对音素发育和随后的影响的细微理解 对可理解性的贡献以前从未可行过，并将对临床和理论产生直接的影响 这意味着什么。结果将为新的评估和治疗奠定基础，以改善 儿童期构音障碍。