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

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.

项目概要/摘要 由于治疗无效或有限，影响/ɹ、s、z/的言语障碍可能会变成慢性的。 长期目标是利用理论和技术进步来加速发展。 寻找可行且有效的治疗方法，以减轻因长期残留言语而导致的生活质量下降 为此，经过验证的基于运动的 RSSD 治疗语音运动链指南。 言语病理学家（SLP）通过高保真、高试验、通过剂量和剂量快速调整治疗 实时操纵运动学习的几个原理是有效的。 对于在传统治疗后仍然存在错误的人来说，至少存在两个挑战：首先， 其次，SLP 需要经过验证的循证实践途径。 案件量大小妨碍了最佳强度因此，该提案的总体目标是优化套件。 尽管有理论动机，但以适当的强度提供高保真、基于运动的治疗 实用，对于占 RSSD 的 90% 的声音：障碍 /ɹ、s、z/ 支持中心工作假设。 根据我们的初步工作，语音电机链接 (a) 在密集交付时更有效（即， （在固定数量的会话中间隔紧密），并且（b）当练习由人工智能主导时也是有益的 gence (AI) SLP 的理论依据是，在治疗早期增加强度将减少错误的实践。 在会议之间，相对于更习惯的实践分布改善结果，并且可靠 人工智能介导的实践在经过验证的治疗背景下是有效的，有以下三个目标： 目标 1：阻止- 我的研究是强化/分布式治疗如何影响 RSSD 中的语音学习。 对照试验 (n=84) 将检验以下假设：强化 SLP 主导的语音运动链（即训练营） 与同等数量的通常分布的语音相比，语音准确性有更大的提高 目标 2：确定语音运动链接练习时 /ɹ/ 生成的改进。 试验由人工智能临床医生领导，将测试多基线单受试者设计。 假设 Chaining-AI（其中 AI SLP 提供临床反馈）有助于具有临床意义的 目标 3：通过优化错误来展示临床人工智能能力的广度。 /s/ 和 /z/ 的发音分类算法将是发音检测算法。 经过训练，可以识别影响 /s/ 和 /z/ 的临床言语错误，用 clini- 复制专家听众的判断 这项重要的研究解决了对理论/经验指导的迫切需求。 针对治疗强度，在约 600 万美国成年人参与的系统中提供急需的建议 这项创新研究加速了 SLP/AI 服务相结合的范式转变。 对于 90% 的 RSSD 来说，交付可以克服有效、可及和充分强化治疗的障碍。