Artificial intelligence–assisted speech therapy (Benway & Preston, 2024)

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 /ɹ/ 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 /ɹ/ productions compared to a no-treatment baseline phase.

Method: Five participants ages 10;7–19;3 (years;months) who were stimulable for /ɹ/ 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 /ɹ/ 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 /ɹ/ to untreated words during the treatment phase compared to the no-treatment baseline. All five participants demonstrated statistically significant generalization of /ɹ/ 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 /ɹ/ 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.

Supplemental Material S1. Supplemental methods.

Supplemental Material S2. Supplemental methods and exploring parent and participant end-user experience.

Benway, N. R., & Preston, J. L. (2024). Artificial intelligence–assisted speech therapy for /ɹ/: A single-case experimental study. American Journal of Speech-Language Pathology, 33(5), 2461–2486. https://doi.org/ 10.1044/2024_AJSLP-23-00448