Deep Learning-Based End-to-End Language Development Screening for Children Using Linguistic Knowledge

Language development is inextricably linked to the development of fundamental human abilities. A language problem can result from abnormal language development in childhood, which has a severe impact on other elements of life. As a result, early treatment of language impairments in children is critical. However, because it is difficult for parents to identify atypical language development in their children, optimal diagnosis and treatment periods are frequently missed. Furthermore, the diagnosis process necessitates a significant amount of time and work. As a consequence, in this study, we present a deep learning-based language development screening model based on word and part-of-speech and investigate the effectiveness of a large-scale language model. For the experiment, we collected data from Korean children by transcribing the utterances of children aged 2, 4, and 6 years. Convolutional neural networks and the notion of Siamese networks, as well as word and part-of-speech information, were used to determine the language development level of children. We also investigated the effectiveness of employing KoBERT and KR-BERT among Korean-specific large-scale language models. In 5-fold cross-validation study, the proposed model has an average accuracy of 78.0%. Furthermore, contrary to predictions, the large-scale language models were shown to be ineffective for representing children's utterances.