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
基本信息
- 批准号:10635488
- 负责人:
- 金额:$ 59.13万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AccelerationAddressAdolescentAdultAffectAgreementAlgorithmsAmericanArticulation DisordersArtificial IntelligenceBehavioral ResearchCase StudyChildChronicClassificationClinicalCommunication impairmentConsensusDetectionDevelopmentDiseaseDoseEducationEmotionalEvidence based practiceExperimental DesignsFeedbackFrequenciesGoalsHumanIndividualInvestigationJudgmentLanguageLearningLongevityMediatingMissionMotorNational Institute on Deafness and Other Communication DisordersOccupationalOutcomeParticipantPathologistPerformanceProductionPublic HealthQuality of lifeRandomized, Controlled TrialsRecommendationResearchResearch DesignResidual stateResourcesSpeechSpeech SoundStimulusSystemTechnologyTeletherapyTestingTherapeuticTimeTrainingTreatment EfficacyTreatment FailureUnited States National Institutes of HealthWorkagedalgorithm trainingclassification algorithmclinical efficacydesigndisabilityeffective therapyevidence baseexperienceimprovedimproved outcomeineffective therapiesinnovationmotor learningoptimal treatmentsservice deliveryskillssocialsoundtoolweb app
项目摘要
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.
项目总结/文摘
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jonathan Preston其他文献
Jonathan Preston的其他文献
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{{ truncateString('Jonathan Preston', 18)}}的其他基金
Treating Childhood Apraxia of Speech: Role of Biofeedback & Practice Distribution
治疗儿童言语失用症:生物反馈的作用
- 批准号:
9377668 - 财政年份:2017
- 资助金额:
$ 59.13万 - 项目类别:
Ultrasound Biofeedback for Therapy-Resistant Speech Sound Disorders in Children
超声生物反馈治疗儿童难治性言语障碍
- 批准号:
8627229 - 财政年份:2013
- 资助金额:
$ 59.13万 - 项目类别:
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