aceArtificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction
ObjectivePerceptual evaluation by speech-language pathologists (SLPs) is essential for initial evaluation of velopharyngeal dysfunction (VPD). Machine learning (ML) offers a promising avenue for developing accessible speech assessment tools when SLP expertise is limited. We aimed to develop a workflow and preliminary algorithm for AI-based hypernasality detection based on the Cleft Audit Protocol for Speech-Augmented-Americleft Modification (CAPS-A-AM), a standardized framework for auditory perceptual speech ass…
Several ML models detected speech-therapist-rated hypernasality in 40 children and teens, suggesting accessible assessment tools where SLP expertise is limited.
Evidence
- Peer-reviewedThe Cleft Palate Craniofacial Journal2026-09-18
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Truvace Impact Record TRV-2026-1140, v2: “Artificial Intelligence-Based Hypernasality Diagnosis Using CAPS-A-AM Rated Speech Samples in Pediatric Velopharyngeal Dysfunction.” Truvace, 2026-10-26. /record/TRV-2026-1140 (accessed at citation time). sha256 5cdf3577fb5ed9b9…
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