Validating an objective assessment of speech outcomes of children with cleft palate pre and post secondary surgery

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Project Summary/Abstract Perceptual assessment of hypernasality is considered a critical component when evaluating the speech of children with cleft lip and/or palate (CLP). However, most speech-language pathologists (SLPs) do not receive formal training for perceptual evaluation of speech and, as a result, research shows that the subjective ratings are inherently biased to the perceiver and exhibit considerable variability. In this project, we aim to validate an artificial intelligence (AI) algorithm that automatically evaluates speech along two dimensions deemed to be critically important by the Americleft Speech Outcomes Group (ASOG), namely hypernasality and speech acceptability. An AI algorithm was developed in an NIH-funded R21 (DE026252, PIs Scherer & Berisha) based on an existing database of 5-7 year olds with CLP from the Americleft Speech Outcomes Project. The AI tool was validated on a small prospective sample of 5-7 year olds with CLP. This proposal has two aims. The first aim will extend the speech samples to children 3-5 years of age with CLP to cover the typical ages when decisions about secondary palatal management are considered and to collect comparison speech samples from noncleft children within this age range. The second aim will validate the AI tool with a cross-sectional and longitudinal sample of children who are receiving a secondary palatal management in a multi-center clinical trial (VPI-OPS, Dr. Thomas Sitzman, PI). The results will provide validation of the automated tool to objectively quantify and track speech production in children with CLP.