Artificial Intelligence-Assisted Speech Therapy for /(sic)/: A Single-Case Experimental Study

Purpose: This feasibility trial describes changes in rhotic production in residual speech sound disorder following ten 40-min sessions including artificial intelligence (AI)-assisted motor-based intervention with ChainingAI, a version of Speech Motor Chaining that predicts clinician perceptual judgment using the PERCEPT-R Classifier (Perceptual Error Rating for the Clinical Evaluation of Phonetic Targets). The primary purpose is to evaluate /(sic)/ productions directly after practice with ChainingAI versus directly before ChainingAI and to evaluate how the overall AI-assisted treatment package may lead to perceptual improvement in /(sic)/ productions compared to a no-treatment baseline phase. Method: Five participants ages 10;7-19;3 (years; months) who were stimulable for /(sic)/ participated in a multiple (no-treatment)-baseline ABA single-case experiment. Prepractice activities were led by a human clinician, and drill-based motor learning practice was automated by ChainingAI. Study outcomes were derived from masked expert listener perceptual ratings of /(sic)/ from treated and untreated utterances recorded during baseline, treatment, and posttreatment sessions. Results: Listeners perceived significantly more rhoticity in practiced utterances after 30 min of ChainingAI, without a clinician, than directly before ChainingAI. Three of five participants showed significant generalization of /(sic)/ to untreated words during the treatment phase compared to the no-treatment baseline. All five participants demonstrated statistically significant generalization of /(sic)/ to untreated words from pretreatment to posttreatment. PERCEPT-clinician rater agreement (i.e., F1 score) was largely within the range of human-human agreement for four of five participants. Survey data indicated that parents and participants felt hybrid computerized-clinician service delivery could facilitate at-home practice. Conclusions: This study provides evidence of participant improvement for /(sic)/ in untreated words in response to an AI-assisted treatment package. The continued development of AI-assisted treatments may someday mitigate barriers precluding access to sufficiently intense speech therapy for individuals with speech sound disorders.