Improving Arabic Dialect Processing in IoT Systems: A Comparative Study of Baseline and Dialect-Aware AI Models

Context: Bringing voice-controlled interfaces into Internet of Things (IoT) systems has created fresh opportunities for smart environments. However, existing voice assistants often struggle with non-standardized languages, especially Arabic dialects. Objective: This research paper explores the challenges and potential of integrating five Arabic dialect variants, namely Modern Standard Arabic (MSA), known as Fusha (الفصحى), Egyptian, Levantine, Gulf, and Algerian dialects, into AI-driven IoT systems. Methods: For each dialect, a comparative simulation was performed using two AI models: a baseline model and a dialect-aware model. Key simulated metrics included automatic speech recognition (ASR) accuracy, intention recognition, task success rate, and system response time. Results: The results consistently show that the dialect-aware model outperforms the baseline model in all metrics. It provides higher ASR and intention recognition accuracy, improved task success rates, and faster response times, especially for regional dialects. The Algerian dialect, while still challenging, benefited significantly from the dialect-aware adaptations of the improved model. These results highlight the potential of dialect-aware AI to close the performance gap caused by linguistic variation and code-switching. Conclusion: This study highlights the importance of considering linguistic diversity when developing accessible, culturally appropriate IoT interfaces that ensure a more inclusive and natural user interaction.