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

项目总结 在大约200万患有中风后失语的美国人中，许多人经历了 言语表达使日常交流变得费力、低效和有压力。1，32 失语症(PWA)，言语不流畅通常被认为是一种明显的残疾，具有显著的社交能力 36、37鉴于这一功能突出，言语流畅性是评估、监测、 还有款待。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， 易于理解且高效的方法7和当前的方法依赖于专家临床医生评分或时间密集型 使用详细编码的语言分析。相比之下，时间声学测量是客观测量 它可以自动地或半自动地从连接的语音中导出。先前的研究已经 证明了语言输出的速度和节奏反映了语言和语言的潜在损害 语言(例如，运动语音、词汇检索)，建议使用时间声学测量来索引 PWA不流利。当前研究的目标是调查使用自动化时态的可行性 识别非流利失语症的声学特征，并更好地理解潜在的语音、语言和 这些表层言语特征背后的认知结构。为了实现这一目标，我们利用机器 作为预测建模方法的一部分的学习技术，以识别其临床用途的语音特征 可以概括为未来对失语症流利性的评估。在目标1中，我们将调查 时间声学特征使用有监督的机器学习方法准确地预测流畅状态 (目标1a)，哪些特征对感兴趣的临床区别最重要(流利与非流利；呈现 无运动性言语障碍；目标1b)。在目标2中，我们将确定潜在的语音、语言和 时间声学测量中个体间可变性的认知贡献者，从而增强了 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，可推广到新的诊断情况。这样做的结果是 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 以及通过将表面水平特征映射到 解释性临床亚结构。

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.