DCNN: Deep Convolutional Neural Network With XAI for Efficient Detection of Specific Language Impairment in Children

Abstract

Assessing children for specific language impairment (SLI) or other communication impairments can be challenging for doctors due to the extensive battery of tests and examinations required. Artificial intelligence and computer-aided diagnostics have aided medical professionals in conducting rapid, reliable assessments of children’s neurodevelopmental conditions concerning language comprehension and output. Previous research has shown differences between the vocal characteristics of typically developing (TD) children and those with SLI. This study aims to develop a natural language processing (NLP) system that can identify children’s early impairments using specific conditions. Our dataset contains examples of disorders, and this study seeks to (1) demonstrate the effectiveness of several classifiers in this regard and (2) select the most effective model from the classifiers. We utilized various machine learning (ML), deep learning (DL), and transformer models to achieve our objective. Our deep convolutional neural network (DCNN) model yielded excellent results, outperforming the competition with an accuracy of 90.47%, making it the top-performing model overall. To increase the accuracy and credibility of our most likely output, we have incorporated explainable AI approaches like SHAP and LIME. These approaches aid in interpreting and explaining model predictions, considering the significance and sensitivity of the topic. Additionally, we believe that our work can contribute to developing more accessible, effective methods for diagnosing language impairments in young children.

Keywords

• Natural language processing
• language impairment
• child
• DCNN
• XAI
• LIME