Preliminary Auditory-Perceptual Evaluation of Resonance for Implementation in an Artificial Intelligence System

Artificial Intelligence and machine learning become more and more crucial for participating in the role of digital entities in various projects. One of these projects could be the resonance disorders and nasality problems as terms referring to a problem that can be recognized during speech production in both children and adults. The previous conditions can be detected by Speech Language Pathologists (SLPs) by using various technology assessment procedures. Especially, low technology procedures are based majorly on the experience of the examiners and their perceptions leading to difficulty in clinical decision-making. The purpose of the current study was to explore the dynamics of implementing an auditory-perceptual evaluation protocol inside an artificial intelligence (AI) system in monolingual Greek-speaking preschool children. The consensus auditory-perceptual evaluation protocol of nasalance was administrated three times to one hundred children and every response was digitally recorded. Three SLPs, after training, evaluated all the participants' recordings based on the predetermined protocol. The results revealed that the AI scheme could be used to improve assessment outcomes of resonance problems in preschool children. The above finding could lead to better and more accurate clinical decision-making and diagnostic accuracy of nasality.