Intensive Speech Motor Chaining Treatment and Artificial Intelligence Integration for Residual Speech Sound Disorders

残余言语障碍的强化言语运动链治疗和人工智能整合

• 批准号: 10635488
• 负责人: Jonathan Preston
• 金额: $ 59.13万
• 依托单位: SYRACUSE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10635488
• 关键词: Acceleration; Address; Adolescent; Adult; Affect; Agreement; Algorithms; American; Articulation Disorders; Artificial Intelligence; Behavioral Research; Case Study; Child; Chronic; Classification; Clinical; Communication impairment; Consensus; Detection; Development; Disease; Dose; Education; Emotional; Evidence based practice; Experimental Designs; Feedback; Frequencies; Goals; Human; Individual; Investigation; Judgment; Language; Learning; Longevity; Mediating; Mission; Motor; National Institute on Deafness and Other Communication Disorders; Occupational; Outcome; Participant; Pathologist; Performance; Production; Public Health; Quality of life; Randomized, Controlled Trials; Recommendation; Research; Research Design; Residual state; Resources; Speech; Speech Sound; Stimulus; System; Technology; Teletherapy; Testing; Therapeutic; Time; Training; Treatment Efficacy; Treatment Failure; United States National Institutes of Health; Work; aged; algorithm training; classification algorithm; clinical efficacy; design; disability; effective therapy; evidence base; experience; improved; improved outcome; ineffective therapies; innovation; motor learning; optimal treatments; service delivery; skills; social; sound; tool; web app; Acceleration; Address; Adolescent; Adult; Affect; Agreement; Algorithms; American; Articulation Disorders; Artificial Intelligence; Behavioral Research; Case Study; Child; Chronic; Classification; Clinical; Communication impairment; Consensus; Detection; Development; Disease; Dose; Education; Emotional; Evidence based practice; Experimental Designs; Feedback; Frequencies; Goals; Human; Individual; Investigation; Judgment; Language; Learning; Longevity; Mediating; Mission; Motor; National Institute on Deafness and Other Communication Disorders; Occupational; Outcome; Participant; Pathologist; Performance; Production; Public Health; Quality of life; Randomized, Controlled Trials; Recommendation; Research; Research Design; Residual state; Resources; Speech; Speech Sound; Stimulus; System; Technology; Teletherapy; Testing; Therapeutic; Time; Training; Treatment Efficacy; Treatment Failure; United States National Institutes of Health; Work; aged; algorithm training; classification algorithm; clinical efficacy; design; disability; effective therapy; evidence base; experience; improved; improved outcome; ineffective therapies; innovation; motor learning; optimal treatments; service delivery; skills; social; sound; tool; web app

Project Summary/Abstract Speech sound disorders impacting /ɹ, s, z/ may become chronic due to either ineffective or limited treat- ment. The long-term goal is to leverage theoretical and technological advancements to accelerate the develop- ment of accessible and effective treatments that mitigate reduced quality of life due to chronic residual speech sound disorders (RSSD). To this end, the validated motor-based RSSD treatment Speech Motor Chaining guides speech-language pathologists (SLPs) through high-fidelity, high-trial, rapidly adapting treatment by dosing and manipulating several principles of motor learning in real time. SLP-led Speech Motor Chaining has been effective for individuals whose errors persist after traditional treatment. However, at least two challenges remain: first, optimal treatment intensity is unknown. Second, SLPs need validated avenues for evidence-based practice when caseload size precludes optimal intensity. Therefore, the overall objective of this proposal is to optimize a suite of theoretically motivated, high-fidelity, motor-based treatments delivered at the appropriate intensity, despite practical barriers, for the sounds comprising 90% of RSSD: /ɹ, s, z/. The central working hypotheses, supported by our preliminary work, are that Speech Motor Chaining is (a) more efficacious when delivered intensively (i.e., closely spaced for a fixed number of sessions), and (b) also beneficial when practice is led by an artificial intelli- gence (AI) SLP. The theoretical rationale is that increasing intensity early in treatment will mitigate erred prac- tice between sessions, improving outcomes relative to more customary practice distributions, and that reliable AI-mediated practice is effective in the context of validated treatments. There are three aims: Aim 1: Deter- mine how intensive/distributed treatment affects speech sound learning in RSSD. A randomized controlled trial (n=84) will test the hypothesis that intensive SLP-led Speech Motor Chaining (i.e., bootcamp) leads to greater gains in speech sound accuracy compared to an equivalent number of customarily distributed sessions. Aim 2: Determine improvement in /ɹ/ production when Speech Motor Chaining practice trials are led by an Artificial Intelligence clinician. A multiple baseline single subject design will test the hypothesis that Chaining-AI, in which an AI SLP provides clinical feedback, facilitates clinically meaningful change in /ɹ/ production. Aim 3: Demonstrate breadth of clinical AI capability by optimizing mis- pronunciation classification algorithms for /s/ and /z/. Mispronunciation detection algorithms will be trained to recognize clinical speech errors affecting /s/ and /z/, replicating expert listener judgement with clini- cally-acceptable accuracy. This significant research addresses a critical need for theoretical/empirical guidance for treatment intensity, offering sorely needed recommendations in a system where ~6 million American adults have unresolved RSSD. This innovative research accelerates a paradigm shift in which combined SLP/AI service delivery could overcome barriers to effective, accessible, and sufficiently intensive treatment, for 90% of RSSD.

项目摘要/摘要 言语障碍影响 /ɹ，s，z /可能由于无效或有限的治疗而变得慢性 精神。长期目标是利用理论和技术进步来加速发展 - 可访问有效的治疗方法可减轻由于长期残留语音而减少生活质量 声音障碍（RSSD）。为此，经过验证的基于电机的RSSD处理语音电动机链条指南 语言病理学家（SLP）通过高保真，高审判，迅速适应治疗 实时操纵运动学习的几种原则。 SLP领导的语音电动机链接已有效 对于那些在传统治疗后持续存在错误的人。但是，至少仍然存在两个挑战：首先， 最佳治疗强度尚不清楚。其次，SLP需要经过验证的途径，以进行循证实践 案件尺寸排除了最佳强度。因此，该提案的总体目标是优化套件 理论上动机，高保真，基于运动的治疗以适当的强度，多叠岩提供 实用的障碍，对于完成RSSD的90％的声音： /ɹ，s，z /。中央工作假设，支持 根据我们的初步工作 在固定数量的会话中近距离），并且（b）当练习由人工智能领导时，也有益 大道（AI）SLP。理论上的理由是，在治疗早期的强度增加将减轻误解的prac- 会议之间的挑战，改善相对于更习惯的实践分布的结果以及可靠的 AI介导的实践在经过验证的治疗范围内有效。有三个目的：目标1：阻止 - 挖掘了RSSD中的强化/分布处理如何影响语音学习。一个随机 对照试验（n = 84）将检验以下假设，即密集的SLP主导电动机链（即训练营） 与常规分布数量相比 会议。目标2：确定语音电动机链接实践时 /ɹ /生产的改进 试验由人工智能临床领导。多个基线单一主题设计将测试 AI SLP提供临床反馈的链接AA的假设促进了临床意义 更改 /ɹ /生产。 AIM 3：通过优化错误来证明临床AI能力的广度 /s /和 /z /的发音分类算法。错误发音检测算法将是 经过培训以识别影响 /s /和 /z /的临床语音错误，通过诊所复制专家听众法官 可以接受的准确性。这项重大研究旨在解决理论/经验指导的关键需求 为了治疗强度，在约600万美国成年人的系统中提供了非常需要的建议 没有解决的RSSD。这项创新的研究加速了SLP/AI服务的范式转变 对于RSSD的90％，交付可以克服有效，可访问和足够密集的治疗的障碍。