Enhancing Conversational AI for Low-Resource Languages: A Case Study on Somali

Conversational AI has made huge strides in understanding and generating human language. However, these advances have mostly benefited high-resource languages such as English and Spanish. In contrast, languages like Somali— spoken by an estimated 20 million people—lack the abundance of annotated data needed to develop robust language models. This study focuses on practical strategies to boost Somali text and speech processing capabilities. We explore three core approaches: (1) transfer learning, (2) synthetic data augmentation, and (3) fine-tuning multilingual models. Our experiments, featuring XLM-R, mBERT, and OpenAI’s Whisper API, show that well-adapted models significantly outperform their baseline counterparts in Somali text translation and speech-to-text tasks. Beyond the numbers, our findings underscore the societal value of creating accessible AI tools for underrepresented linguistic communities, providing a template for extending these methods to other low-resource languages.