Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

Eugenia I. Toki, Giorgos Tatsis, Vasileios A. Tatsis, Konstantinos Plachouras, Jenny Pange and Ioannis G. Tsoulos ()
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Eugenia I. Toki: Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Panepistimioupoli B’, 45500 Ioannina, Greece
Giorgos Tatsis: Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Panepistimioupoli B’, 45500 Ioannina, Greece
Vasileios A. Tatsis: Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Panepistimioupoli B’, 45500 Ioannina, Greece
Konstantinos Plachouras: Department of Speech and Language Therapy, School of Health Sciences, University of Ioannina, Panepistimioupoli B’, 45500 Ioannina, Greece
Jenny Pange: Laboratory of New Technologies and Distance Learning, Department of Early Childhood Education, School of Education, University of Ioannina, 45110 Ioannina, Greece
Ioannis G. Tsoulos: Department of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, Greece

Mathematics, 2023, vol. 11, issue 7, 1-15

Abstract: Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders.

Keywords: SmartSpeech; neural networks; optimization; genetic algorithms; biometrical data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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