AI-Powered Insights and Processing for Kurdish Dialects: Connecting Sorani and Bahdini through Advanced Linguistic Technologies

Abstract

Metaheuristic The application of AI-driven techniques to Kurdish dialects, particularly Sorani and Bahdini, presents unique challenges in natural language processing (NLP) due to their phonological and syntactical complexities. This paper critically examines existing research focused on the AI-based analysis and processing of these dialects, comparing various methodologies and algorithms employed. From traditional statistical models to modern deep learning approaches, such as transformer architectures, we explore how AI can effectively differentiate and process these dialects. Our comparative study reveals that transformer-based models offer significant improvements over older techniques, leading to enhanced dialect recognition accuracy. This research also identifies key areas for future exploration, aiming to further refine AI models for improved applications in machine translation, speech recognition, and sentiment analysis across Sorani and Bahdini dialects. By bridging the gap between these dialects through AI, this work paves the way for more inclusive and accurate linguistic technologies for Kurdish-speaking communities.