Exploring the Speech Language Therapy through Information Communication Technologies, Machine Learning and Neural Networks

Currently, the needs for new technologies are rising and especially due to Covid-19. The world is changing into a digital theater of businesses and educational processes. Consequently, tele-health is constantly enriched with new scientific areas of expertise, a fact that gives a new and promising perspective for people who cannot reach in-time a specialist. Specifically, for the case of speech language therapy (SLT), new techniques emerge and involve telepractice and tele-counceling. In turn, the aim of the present study was to discover a model of predicting potential benefits from the online form of speech and language therapy (distant sessions) along with the view of the involved speech therapists and parents. For this purpose, a hypothetical sample of specialists and parents, was produced as being supposedly participated into sessions with their children for evaluating the overall performance of each technique (live and distant) with a total score. Thereinafter, statistical analyses were performed and the relevant machine learning was applied for building an artificial neural network for examining the results, in order to show the capabilities of the proposed model. This outcome is important as it can actuate SLT scientific area to include, in the future, more advanced techniques for distant sessions, concluding to even surpassing the classical technique.