ArticleAmerican journal of speech-language pathology2024
Artificial Intelligence-Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study.
Article in American journal of speech-language pathology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rhotic Generalization Is More Rapid in Biofeedback Than Motor-Based Treatment for Residual Speech Sound Disorder: Secondary Outcomes of a Randomized Controlled Trial.Journal of speech, language, and hearing research : JSLHR · 2026Trial
- Perceptual Ratings Predict Speech Inversion Articulatory Kinematics in Childhood Speech Sound Disorders.Journal of speech, language, and hearing research : JSLHR · 2026Article
- An End-to-End Overview of Clinical Speech AI.IEEE transactions on audio, speech, and language processing (2025) · 2026Article
- Transforming Speech-Language Pathology with AI: Opportunities, Challenges, and Ethical Guidelines.Healthcare (Basel, Switzerland) · 2025Review
- Machine Learning-Based Identification of Phonological Biomarkers for Speech Sound Disorders in Saudi Arabic-Speaking Children.Diagnostics (Basel, Switzerland) · 2025Article
- Reevaluating the classification of pediatric speech sound disorders: a ground truthing perspective.Frontiers in human neuroscience · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
purposeThis 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.
methodFive 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.
resultsListeners 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.
conclusionsThis 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.
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