Fine-Tuning Large Language Models for Saudi Arabic Voice Agents

Abstract

To support the growing voice-focused technologies in Saudi Arabia, such as innovative city solutions, government services, healthcare, and finance that require voice-assisted search and navigation, there is a need to create voice agents that will provide linguistic and cultural accuracy with high linguistic clarity and understanding of the Saudi Arabian culture. Historical systems of natural language processing (NLP) that are usually trained over Modern Standard Arabic (MSA) or generalized (dialect) corpus in general were not necessarily capable of representing the regional, phonetic, and pragmatic peculiarities of Saudi Arabic across quite different Najdi, Hijazi, Gulf, and Southern dialects. This paper discusses fine-tuning the large language models (LLMs) to enable productive spoken-dialog systems tailored to the Saudi users.

It incorporates intensive data collections with the help of multiple Saudi Arabic sources, labeling data by dialect, preprocessing the acoustic features, and fine-tuning (several stages) of transformer-based systems. The method entails the hybrid training with textual and audio data, and the performance assessment is carried out using both automatic measures (e.g., WER, BLEU) and human expertise of the trustworthiness, fluency, and sociocultural compatibility. The practical result shows that fine-tuned models can bring a far greater accuracy than baseline MSA or generic Arabic models in particular domains of use like e-government services, travel agencies specializing in religion, and triaging healthcare systems. Issues such as ethics and practicality of fairness of dialect representation, privacy of voice data, and sociolinguistic bias are crucial ethical and practical issues that the author discusses in the paper. In addition to being usable, voice agents would require cultural competence to make them inclusive and digitally equitable.

This work presents a language-aware framework to support regional language learning options in Saudi Arabian, which further provides a blueprint that can be scaled to localize the use of LLMs in less-representative linguistic contexts, and a portion of the Saudi Arabian government is likely to meet its overall AI and digital transformation vision as outlined in Vision 2030.