: Natural language processing (NLP) ranks among the most important areas of contemporary linguistic research, due to its epistemological intersection between theoretical linguistics and computer science. This interaction has given rise to computational linguistics as an interdisciplinary science aimed at building formal and algorithmic models that simulate human linguistic competence in comprehension and production. Within this framework, the issue of automatic processing of the Arabic language emerges as particularly challenging. Despite technological advancements, Arabic NLP remains in its foundational stages, especially regarding the semantic and prosodic components. The limited progress in Arabic speech processing is largely due to the absence of precise engineering descriptions of prosodic features, such as stress and intonation, which play a central role in constructing pragmatic meaning and distinguishing the utterance-level structure of sentences. The lack of formal and mathematical modeling of these phenomena, alongside the scarcity of standardized speech databases, hinders effective automatic processing of Arabic speech and prevents machines from achieving the perceptual and generative competence required for intelligent natural language simulation. This study aims to highlight the importance of integrating prosodic aspects into the computational architecture for Arabic speech processing, considering it a crucial step toward developing more coherent and linguistically appropriate systems for language perception and generation.