AI-Assisted Treatment for Residual Speech Sound Disorders
Start Date
9/5/2024
Completion Date
12/31/2027
Summary
The goal of this randomized-controlled trial is to determine how artificial intelligence-assisted home practice may enhance speech learning of the "r" sound in school-age children with residual speech sound disorders. All child participants will receive 1 speech lesson per week, via telepractice, for 5 weeks with a human speech-language clinician. Some participants will receive 3 speech sessions per week with an Artificial Intelligence (AI)-clinician during the same 5 weeks as the human clinician sessions (CONCURRENT treatment order group), whereas others will receive 3 speech sessions per week with an AI-clinician after the human clinician sessions end (SEQUENTIAL treatment order group.
Detailed Description
Artificial Intelligence-assisted treatment that detects mispronunciations within an evidence-based motor learning framework could increase access to sufficiently intense, efficacious treatment despite provider shortages. A successful Artificial intelligencesystem that can predict the clinical gold standard of trained listeners' perceptions could not only improve access to clinical care but also mitigate known confounds to accurate clinical feedback, including clinical experience and drift due to increasing familiarity between the speaker and listener. The Artificial intelligence tool used in this study includes a speech classifier trained to predict clinician judgment of American English "r" that is integrated into an existing evidence-based treatment software called Speech Motor Chaining.
Eligibility Criteria
Age Range: 9 years to 17 years
Interventions
Speech-Language Pathologist-led Speech Motor Chaining
Artificial Intelligence-led Speech Motor Chaining (CHAINING-AI)
Conditions
Locations
Syracuse University
Syracuse, New York 13244
United States