The Comoros Islands, rich in linguistic diversity, are home to dialects derived from Swahili and influenced by Arabic. Historically, the Kamar-Eddine system, based on the Arabic alphabet, was one of the first writing systems used for Comorian. However, it has gradually been replaced by the Latin alphabet, even though numerous archival texts are written in this system, and older speakers continue to use it, highlighting its cultural and historical significance. In this article, we present Shialifube, a bidirectional transliteration tool between Latin and Arabic scripts, designed in accordance with the rules of the Kamar-Eddine system. To evaluate its performance, we applied a round-trip transliteration technique, achieving a word error rate of 14.84% and a character error rate of 9.56%. These results demonstrate the reliability of our system for complex tasks. Furthermore, Shialifube was tested in a practical case related to speech recognition, showcasing its potential in Natural Language Processing. This project serves as a bridge between tradition and modernity, contributing to the preservation of Comorian linguistic heritage while paving the way for better integration of local dialects into advanced technologies.

In this era of extensive digitalization, there are a profusion of Intelligent Systems that attempt to understand how languages are structured for the aim of providing solutions in various tasks like Text Summarization, Sentiment Analysis, Speech Recognition, etc. But for multiple reasons going from lack of data to the nonexistence of initiatives, these applications are in an embryonic stage in certain languages and dialects, especially those spoken in the African continent, like Comorian dialects. Today, thanks to the improvement of Pre-trained Large Language Models, a spacious way is open to enable these kind of technologies on these languages. In this study, we are pioneering the representation of Comorian dialects in the field of Natural Language Processing (NLP) by constructing datasets (Lexicons, Speech Recognition and Raw Text datasets) that could be used on different tasks. We also measure the impact of using pre-trained models on languages closely related to Comorian dialects to enhance the state-of-the-art in NLP for these latter, compared to using pre-trained models on languages that may not necessarily be close to these dialects. We construct models covering the following use cases: Language Identification, Sentiment Analysis, Part-Of-Speech Tagging, and Speech Recognition. Ultimately, we hope that these solutions can catalyze the improvement of similar initiatives in Comorian dialects and in languages facing similar challenges.