Artificial Intelligence-Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study.
| Autor: | Benway NR; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD., Preston JL; Department of Communication Sciences and Disorders, Syracuse University, NY. |
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| Jazyk: | angličtina |
| Zdroj: | American journal of speech-language pathology [Am J Speech Lang Pathol] 2024 Sep 18; Vol. 33 (5), pp. 2461-2486. Date of Electronic Publication: 2024 Aug 22. |
| DOI: | 10.1044/2024_AJSLP-23-00448 |
| Abstrakt: | 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: https://doi.org/10.23641/asha.26662807. |
| Databáze: | MEDLINE |
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