Instrumental and Machine Learning Analysis Approaches to Speech Sound Disorder Assessment in Arabic-Speaking Children: A Review With Narrative Synthesis

Purpose: This review article provides a narrative synthesis of instrumental and machine learning (ML) approaches used to study speech sound development and disorders, with emphasis on Arabic-speaking children. Research in this area draws on multiple disciplines, including acoustic and articulatory phonetics, computer science, linguistics, psychology, and clinical speech-language pathology. The review aims to summarize available methodologies and highlight considerations for applying them in Arabic-speaking contexts. Method: Instrumental and computational methods reviewed include acoustic analysis, electropalatography, ultrasound tongue imaging, computerized analysis tools (e.g., Phon, Computerized Language Analysis, Praat), and ML-based techniques such as probabilistic models and neural networks. Key research designs and strategies commonly employed in the study of speech sound disorders (SSDs) are also discussed, with references to representative studies. Conclusions: This review outlines the methodological options available to researchers and clinicians, emphasizing how each contributes to accurate and culturally responsive SSD assessment. Traditional perceptual approaches remain central but benefit from integration with instrumental and computational methods, which provide objective data and scalable solutions. For Arabic-speaking children, the development of dialect-sensitive corpora and normative data sets is essential to ensure diagnostic validity. By synthesizing current methods, this review informs both research design and clinical practice, supporting more equitable and linguistically appropriate assessment of SSDs.