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

项目摘要 一半的脑瘫儿童（CP）有构音障碍，这对他们的生活有很好的负面影响。 语音清晰度治疗的主要目的是提高语音清晰度。当前儿科构音障碍 干预集中于清晰和/或响亮的语音、发声、语速或这些的某种组合。而 一些干预措施已导致清晰度的提高，但结果存在很大差异。额外 需要采取干预措施，以确保所有儿童都能最大限度地提高语言的可理解性。实验室研究 一致认为发音系统是构音障碍中可懂度缺陷的最大贡献者， 涉及诸如元音空间和F2斜率的变量。然而，这些措施在临床上是不可用的， 也不能转化为具体的治疗目标。考虑到发音系统对可理解性的重要性 和发展的可塑性与儿童的语言习得，有必要 使用临床上有意义的单位来量化关节的敏感度量（即，语音）。这样的度量 将使我们能够理解不同的语音如何有助于可理解性，并将导致 在开发数据驱动的干预措施以提高患有自闭症儿童的可理解性方面取得的进展 构音障碍，补充现有的治疗方法。我们的目标是开发一种临床工具， 连续的，自动量化的语音清晰度从连接的语音在儿童;我们 将使用这个工具来量化单个音素对可懂度的贡献。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，以及 语音病理学，以细化和验证为每个语音病理学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来创建音素清晰度发展的增长曲线 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， 描述这些儿童中各个音素对可懂度的贡献。然后我们将 检查700个来自2岁半至10岁之间的构音障碍儿童的纵向语音样本， 并确定他们与典型儿童在音素发展和语音发音方面的差异 有助于理解。这项研究的成果是一个算法，可以量化音素清晰度 在儿童中，表示儿童相对于每个音素的标准的表现。结果将指定相对 单个音素对可懂度的贡献，以及这种贡献如何在发展过程中和背景下发生变化。 构音障碍对构音障碍对音素发展和后续影响的精细理解 对可懂度的贡献以前从未可行过，并且将具有直接的临床和理论意义。 影响研究结果将为新的评估和治疗奠定基础，以提高语音的可懂度。 儿童构音障碍。

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.