An Automated Model for Child Language Impairment Prediction Using Hybrid Optimal BiLSTM

Abstract

Children without obvious disabilities (hearing loss/low intellectual capacity) may have language skill development issues due to specific language impairment (SLI), a communication disorder. The SLI has a significant impact on a child's speaking, listening, reading, and writing abilities. SLI is typically known as development language disorder, developmental dysphasia, or language delay. Recently, machine learning as well as deep learning techniques have been quite effective in predicting the early stage of SLI, analyzing the disorder severity, and predicting the treatment efficiency. Existing approaches primarily exploited auditory indicators to diagnose communication disorders, frequently leaving out hidden information acquired in the temporal domain. To overcome this drawback, an optimized Bidirectional Long Short Term Memory (BiLSTM) architecture is presented in this paper to handle the speech dynamics. The Improved Hybrid Aquila Optimizer and Flow Directional algorithm known as IHAOFDA is integrated with the BiLSTM architecture to optimize the hyperparameters of the BiLSTM structure. When assessed using the information from the SLI children in the Laboratory of Artificial Neural Network Applications (LANNA) dataset, the proposed model performs better. The IHAOFDA-optimized BiLSTM architecture improves accuracy in classifying different severity levels such as mild, moderate, and severe.