AI-Assisted Treatment for Residual Speech Sound Disorders

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.

Conditions

• Speech Sound Disorder

Interventions

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.

Sponsors & Collaborators

• National Institutes of Health (NIH)

  collaborator NIH

• State University of New York - Upstate Medical University

  collaborator OTHER

• National Institute on Deafness and Other Communication Disorders (NIDCD)

  collaborator NIH

• Syracuse University

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

9 Years

Max Age

17 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-09-05

Primary Completion

2027-12-31

Completion

2027-12-31

Countries

• United States

Study Locations