Perception of dysarthric speech: An objective model of dysarthric speech evaluation with actionable outcomes

构音障碍言语的感知：具有可操作结果的构音障碍言语评估的客观模型

• 批准号: 9312085
• 负责人: Visar Berisha
• 金额: $ 31.15万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9312085
• 关键词: Acoustics; Adopted; Affect; Algorithms; Area; Attention; Caring; Clinical; Clinical Assessment Tool; Code; Cognitive; Communication impairment; Complex; Computer Simulation; Country; Cues; Custom; Data; Dimensions; Disease Progression; Dysarthria; Ear; Educational Intervention; Evaluation; Frequencies; Genetic Transcription; Goals; Gold; Grant; Health Services Accessibility; Individual; Instruction; Intervention; Judgment; Knowledge; Language; Learning; Loudness; Machine Learning; Measures; Modeling; Motor; Nervous System Trauma; Noise; Outcome; Outcome Measure; Participant; Pathologist; Patient Monitoring; Patients; Pattern; Perception; Periodicity; Population; Process; Recruitment Activity; Research; Sampling; Severities; Signal Transduction; Source; Speech; Speech Acoustics; Speech Disorders; Speech Intelligibility; Speech Pathology; Speech Perception; Speech-Language Pathology; Stimulus; Stream; Technology; Testing; Theoretical model; Time; Training; Update; Validation; Work; base; clinical practice; health disparity; improved; lexical; nervous system disorder; novel; outcome prediction; phrases; predictive modeling; signal processing; standard of care; tool

The trained ear of the speech-language pathologist is the gold standard assessment tool for clinical practice in motor speech disorders. However, perceptual judgments are vulnerable to bias and their relationship with estimates of listener intelligibility – the final arbiter of speech goodness – is indeterminate. Interpretable, objective, and robust outcome measures that provide targets for treatment are urgently needed in order to provide more precise care and reliably monitor patient progress. Based on theoretical models of speech perception, in our previous grants we have developed a novel set of outcome measures that provide a multi- dimensional intelligibility profile (MIP) by using custom speech stimuli and a new coding strategy that allows us to capture the types of errors that listeners make when listening to dysarthric speech. This has led to a more complete intelligibility profile that codifies these errors at different levels of granularity, from global to discrete. Simultaneously, we have also developed a computational model for evaluation of dysarthric speech capable of reliably estimating a limited set of intelligibility measures directly from the speech acoustics. To date, both the outcome measures and the objective model have been evaluated on cross-sectional data only. In this renewal application, our principal goal is to evaluate specific hypotheses regarding expected changes in this multidimensional intelligibility profile as a result of different intervention instruction conditions (loud, clear, slow). A secondary goal of the proposal is to further refine our objective model to predict the complete intelligibility profile and to evaluate its ability to detect intelligibility changes within individual speakers. This is critical for clinicians who currently have no objective ways to assess the value of their interventions. With the aim of improving the standard of care through technology, the long-term goal of this proposal is to develop stand-alone objective outcome measures for dysarthria that can provide clinicians with reliable treatment targets. Such applications have the potential to dramatically alter the current standard of care in speech pathology for patients with neurological disease or injury. Furthermore, these applications also have the potential to reduce health disparities by partially automating clinical intervention and providing easier access to these services to those in remote areas or in underdeveloped countries.

言语语言病理学家经过训练的耳朵是临床实践的黄金标准评估工具， 运动性言语障碍然而，感性判断容易受到偏见和他们的关系， 对听者可懂度的估计--语音质量的最终仲裁者--是不确定的。可解释的， 迫切需要提供治疗目标的客观和强有力的结果指标， 提供更精确的护理并可靠地监测患者的进展。基于言语的理论模型 感知，在我们以前的赠款，我们已经开发了一套新的结果的措施，提供了一个多- 三维清晰度轮廓（MIP）通过使用自定义语音刺激和一种新的编码策略，使我们 来捕捉听众在听构音障碍的演讲时所犯的错误类型。这导致了一个更 完整的可懂度配置文件，以不同的粒度级别（从全局到离散）对这些错误进行编码。 同时，我们还开发了一个用于评估构音障碍言语的计算模型， 直接从语音声学可靠地估计可懂度测量的有限集合。迄今为止， 仅根据横截面数据对结果测量和客观模型进行了评价。在这次更新中， 应用程序，我们的主要目标是评估有关预期变化的具体假设， 作为不同干预指令条件（大声，清晰， 慢）。该提案的第二个目标是进一步完善我们的客观模型，以预测完整的 可理解性配置文件，并评估其能力，以检测清晰度的变化，在个别发言人。这是 对于目前没有客观方法评估其干预价值的临床医生至关重要。与 为了通过技术提高护理标准，该提案的长期目标是发展 构音障碍的独立客观结局指标，可为临床医生提供可靠的治疗 目标的这种应用有可能极大地改变目前的言语护理标准 神经系统疾病或损伤患者的病理学。此外，这些应用程序还具有 通过部分自动化临床干预和提供更容易获得的服务， 这些服务提供给偏远地区或不发达国家的人们。