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

使用机器学习对失语症患者的不流利言语进行检测和分类

• 批准号: 10647054
• 负责人: Claire Elizabeth Cordella
• 金额: $ 0.25万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10647054
• 关键词: Acoustics; Affect; American; Aphasia; Articulation; Clinical; Code; Cognitive; Communication; Databases; Diagnostic; Feeling; Functional disorder; Future; Goals; Impaired cognition; Impairment; Individual; Intervention; Language; Life; Light; Linear Regressions; Linguistics; Long-Term Effects; Machine Learning; Measures; Mental Depression; Modeling; Monitor; Motor; Output; Patients; Persons; Population; Predictive Value; Proxy; Research; Sampling; Scheme; Social isolation; Source; Speech; Standardization; Surface; Techniques; Testing; Time; Training; Validation; Work; accurate diagnosis; cognitive function; cohort; disability; experience; functional outcomes; indexing; inter-individual variation; interest; language impairment; lexical retrieval; machine learning classifier; novel; novel diagnostics; novel strategies; post stroke; predictive modeling; prospective; recruit; social; stroke-induced aphasia; supervised learning; syntax; treatment planning; treatment response

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.

项目摘要 在大约200万美国人患有势后失语症的美国人中，许多人遇到困难 口头表达，使日常沟通努力，效率低下和压力很大。1,32 失语症（PWA），语音非浮力通常是一种可见的残疾，具有重要的社会性残疾 后果36,37鉴于这种功能显着性，语音流利性是评估，监视， 和对待。但是，这是一种综合性的索引流利度的长期临床挑战 可解释，高效的7和当前方法依赖于专家临床医生评级或时间密集型 使用详细编码进行语言分析。相比之下，颞声措施是客观的措施 可以自动或半自动从连接的语音派生。先前的研究已有 证明语音输出的速度和节奏反映了语音和 语言（例如，电机演讲，词汇检索），暗示了时间声音索引的实用性 PWA中的非浮力。本研究的目的是研究使用自动化时间的可行性 声学特征以识别非素质失语症，并更好地了解潜在的语音，语言和 这些表面语音特征的背后的认知结构。为了实现这一目标，我们利用机器 学习技术是一种预测建模方法的一部分，以识别其临床实用程序的语音特征 可以概括以告知未来对失语中流利的评估。在AIM 1中，我们将调查是否 暂时的声学特征可以使用监督的机器学习方法准确预测流利度状态 （AIM 1A），以及哪些特征对于感兴趣的临床区别最重要（Fluentv。Non-Fluent;目前 v。缺乏运动言语障碍；目标1b）。在AIM 2中，我们将确定基本的语音，语言和 暂时声学措施中个体变异性的认知贡献者，从而增加 研究结果的解释能力。这些目标是迈向可解释和自动的第一步 可以推广到新型诊断情况的PWA流利度的预测模型。结果 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 患者以及通过将这些功能映射到 解释性临床子结构。