AI-Assisted Treatment for Residual Speech Sound Disorders

Status Recruiting

Clinical Trial 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.

Clinical Trial 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. ;

Study Design

Related Conditions & MeSH terms

Speech Sound Disorder

Clinical Trial Details

NCT number	NCT05988515
Study type	Interventional
Source	Syracuse University
Contact	Jonathan Preston, PhD
Phone	315-443-1351
Email	jopresto@syr.edu
Status	Recruiting
Phase	N/A
Start date	March 12, 2024
Completion date	December 2027

View Details

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