Detecting and classifying non-fluent speech in aphasia using machine learning

项目摘要 在大约200万患有中风后失语症的美国人中，许多人在治疗方面遇到困难。 使日常沟通费力、低效和紧张的语言表达。1，32 在失语症（PWA）中，言语不流利通常被认为是一种明显的残疾， 考虑到这种功能性的显著性，言语流畅性是评估，监测， 然而，以一种全面的方式索引流畅性是一个长期的临床挑战， 可解释的，有效的，7和目前的方法依赖于专家临床医生的评级或时间密集型 使用详细编码进行语言分析。相比之下，时间声学测量是客观测量 可以自动或半自动地从连接的语音中导出。先前的研究 表明，语音输出的速率和节奏反映了语音和 语言（例如，运动言语，词汇检索），这表明时间声学措施索引的效用 PWA不流畅。本研究的目的是调查使用自动时态的可行性 声学特征，以识别非流利性失语症，并更好地了解潜在的语音，语言， 这些表面语音特征背后的认知结构。为了实现这一目标，我们利用机器 学习技术作为预测建模方法的一部分，以识别其临床效用的语音特征， 可以概括为未来的失语症流利性评估提供信息。在目标1中，我们将研究 时间声学特征使用监督机器学习方法准确地预测流畅性状态 (Aim 1a），以及哪些特征对感兴趣的临床区别最重要（流利与非流利;存在 诉运动性言语缺损;目标1b）。在目标2中，我们将确定潜在的言语、语言和 认知贡献者的个体间变异性的时间声学措施，从而增加了 研究结果的解释力。这些目标是迈向可解释和可自动化的第一步。 PWA流畅性的预测模型，可以推广到新的诊断情况。成果 研究将帮助临床医生确定有效评估和治疗计划的重要特征， 以及通过将这些特征映射到 解释性临床子结构。

PROJECT SUMMARY Among the approximately 2 million Americans living with post-stroke aphasia, many experience difficulties with verbal expression that render everyday communication effortful, inefficient, and stressful.1,32 For persons with aphasia (PWA), speech non-fluency is often experienced as a visible disability with significant social consequences.36,37 Given this functional salience, speech fluency is an important construct to assess, monitor, and treat. It is, however, a longstanding clinical challenge to index fluency in a way that is comprehensive, interpretable, and efficient,7 and current approaches rely on either expert clinician ratings or time-intensive linguistic analyses using detailed coding. Temporal acoustic measures, by contrast, are objective measures that can be automatically or semi-automatically derived from connected speech. Prior research has demonstrated that the rate and rhythm of speech output reflect underlying impairments in both speech and language (e.g., motor speech, lexical retrieval), suggesting the utility of temporal acoustic measures to index non-fluency in PWA. The goal of the current study is to investigate the feasibility of using automated temporal acoustic features to identify non-fluent aphasia and to better understand the latent speech, language, and cognitive constructs underlying these surface speech features. To achieve this goal, we leverage machine learning techniques as part of a predictive modeling approach to identify speech features whose clinical utility can be generalized to inform future assessment of fluency in aphasia. In Aim 1, we will investigate whether temporal acoustic features accurately predict fluency status using a supervised machine learning approach (Aim 1a), and which features are most important to clinical distinctions of interest (fluent v. non-fluent; present v. absent motor speech impairment; Aim 1b). In Aim 2, we will determine the underlying speech, language, and cognitive contributors to inter-individual variability in temporal acoustic measures, thereby augmenting the explanatory power of study results. These aims are a first step toward an interpretable and automatable predictive model of fluency in PWA that can be generalized to novel diagnostic situations. Results of this research will help clinicians identify important features for efficient assessment of and treatment planning for patients as well as provide a mechanistic understanding of surface level features by mapping those features to explanatory clinical sub-constructs.