NCT05988515

AI-Assisted Treatment for Residual Speech Sound Disorders

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 26 (estimated)
• Sponsor: Syracuse University · Academic / Other
• Sex: All
• Age: 9 Years – 17 Years
• Healthy volunteers: Accepted

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.

Conditions

• Speech Sound Disorder

Interventions

Type	Name	Description
BEHAVIORAL	Speech-Language Pathologist-led Speech Motor Chaining	Sessions begin with Pre-practice to elicit the /r/ sound. During Structured Practice, the same utterance is practiced several times in a row (with systematic increases in difficulty based on performance). Our web-based software manipulates the principles of motor learning, including feedback prompts for the clinician, the complexity of the utterance, and the variability in the practice trial; the software will analyze the clinician's rating to increase the difficulty of practice when the child is more accurate. Randomized Practice will also be guided by the software and includes all linguistic levels that were produced correctly during Structured Practice, with items presented in random order. A trained speech-language pathologist is involved in all practice trials to provide feedback throughout the session.
BEHAVIORAL	Artificial Intelligence-led Speech Motor Chaining (CHAINING-AI)	Sessions include Structured Practice and Randomized Practice using our web-based software with an Artificial Intelligence clinician to address the /r/ sound. Within a practice session, participants speak into a microphone, and the audio file is sent to a server to be analyzed by a classifier, which returns a binary accurate/inaccurate rating of productions in a fashion similar to SLP judgment. Our web-based software manipulates the principles of motor learning, including feedback prompts, the complexity of the utterance, and the variability in the practice trial. The software will analyze the child's accuracy as determined by the classifier to increase the difficulty of practice when the child is more accurate.

Timeline

• Start date: 2024-09-05
• Primary completion: 2027-12-31
• Completion: 2027-12-31
• First posted: 2023-08-14
• Last updated: 2026-01-08

Locations

1 site across 1 country: United States