Artificial Intelligence–Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study

Nina R. Benway, Jonathan L. Preston

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: This feasibility trial describes changes in rhotic production in residual speech sound disorder following ten 40-min sessions including artificial intelli-gence (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 improve-ment 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 experi-ment. 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 agree-ment for four of five participants. Survey data indicated that parents and partici-pants 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 contin-ued development of AI-assisted treatments may someday mitigate barriers pre-cluding access to sufficiently intense speech therapy for individuals with speech sound disorders.

Original languageEnglish (US)
Pages (from-to)2461-2486
Number of pages26
JournalAmerican journal of speech-language pathology
Volume33
Issue number5
DOIs
StatePublished - Sep 2024

ASJC Scopus subject areas

  • Otorhinolaryngology
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Speech and Hearing

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