AI and Dialect Recognition: Challenges and Opportunities in Linguistic Diversity
Shaliza Alwi, Masrina Nadia Mohd Saleh, Mohamad Naqiuddin Md Mansor · Forum for Linguistic Studies · 2025
This article explores the intersection of artificial intelligence (AI) and dialect recognition, highlighting both the challenges and opportunities presented by linguistic diversity. As globalization increases, the need for effective communication across dialects becomes paramount. Recent advancements in machine learning, particularly deep learning, have transformed the capabilities of AI in understanding and processing human language. This article examines the current state of dialect recognition technologies, the linguistic complexities involved, and the implications for social inclusion and technological advancement. By analyzing recent studies (2013–2024), this article aims to provide insights into the future of AI-driven dialect recognition systems, identifying key areas for further research and development. The study highlights significant breakthroughs in artificial intelligence-driven terminology recognition tools. Important problems include the inability of technology to generalise over a wide range of dialects, the under-representation of low-resource dialects in training datasets, and biases that reflect cultural preconceptions. These difficulties give rise to concerns regarding inclusivity and fairness, as well as the fact that marginalised communities frequently experience failures in acknowledgement. To enhance cultural sensitivity and trust, the study underscores the significance of ethical frameworks that prioritise diversity, interdisciplinary collaboration with linguists and sociologists, and community engagement. Transfer learning is a promising solution for the mitigation of low-resource dialects and the preservation of linguistic diversity. The article also emphasises the importance of ongoing monitoring to accommodate evolving sociocultural and linguistic environments, as well as the ethical implications of implementing such technologies in sensitive situations.