Preserving Comorian Linguistic Heritage: Bidirectional Transliteration Between the L atin Alphabet and the K amar-Eddine System (2025)

Abstract: 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 However, it has gradually been replaced by the Latin alphabet, even though numerous archival texts are written in this system, and older speakers continue to use it, highlighting its cultural and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.

## introduction at the crossroads of africa, europe, the middle east, and southeast asia @xcite , the comoros stand out for their rich cultural heritage, a diversity particularly evident in local dialects that share remarkable similarities with several foreign languages. while these dialects belong to the bantu family due to their closer affinity with swahili (ahmed chamanga, 2022) and the sabaki language group @xcite , they also exhibit similarities with arabic. this is partly why, like swahili with the ajami script @xcite , one of the earliest writing systems for comorian dialects is based on the arabic script @xcite . known as the kamar-eddine system, this writing system was introduced in the 1960s by linguist sheikh ahmed kamar-eddine. although the latin alphabet is now predominantly used to write comorian, a minority, primarily older individuals, are only proficient in the arabic script. furthermore, many manuscripts are written in this script, emphasizing its historical and cultural significance. having a solution to process this system could serve three major purposes: (a) democratizing access to natural language processing (nlp) technologies, making comorian dialects accessible to a broader audience, especially those without access to modern digital tools; (b) preserving and promoting the multicultural richness of the archipelago, highlighting the kamar-eddine system as a fundamental element of comorian linguistic and cultural heritage; (c) making comorian national archives accessible to all, facilitating their digitization and long-term preservation while paving the way for new research and applications in nlp. this work aims to initiate nlp research for this writing system, with the hope of contributing to the preservation of comorian intangible heritage. more concretely, our main contributions can be summarized as follows: ## about shikomori comorian, or shikomori, consists of four dialects, each spoken on a specific island: shingazidja, shimwali, shindzuani, and shimaore. while ideally, each dialect would be treated individually, this work addresses comorian as a whole, without distinguishing between its dialectal variations. two main reasons justify this choice: the dialects are very closely related (ahmed chamanga, 2022). consequently, a speaker from one island can understand a dialect spoken on another island with little difficulty due to the largely shared lexicon across these variants. this strong similarity facilitates the development of nlp solutions that can generalize across all dialects. the high similarities among these dialects, combined with the significant lack of data, make it more practical to treat them as a single language. attempting to develop solutions for each dialect individually would require working with small, separate corpora, which might not suffice for training effective models. instead, this approach leverages data-rich dialects to improve performance on those with fewer resources. this strategy aligns with the findings of lin et al. @xcite , which explored multilingual transfer learning as a means to improve low-resource language representation by leveraging a well-resourced language with significant similarities. additionally, the system introduced by kamar-eddine considers comorian as a unified language, with no specific rules tailored to individual dialects. ## related work comorian is a language that has been very little studied in the field of nlp. while some previous works have provided solutions addressing it for various use cases @xcite , to the best of our knowledge, there is no computational linguistics research that deals with the language in its arabic script. beyond our desire to preserve this intangible heritage, there is a motivation arising from observations made in previous works, such as those found in @xcite . the latter describes experiments conducted on maltese in which a curious observation was made: in several tasks (named entity recognition, sentiment analysis, etc.), transliteration into arabic characters significantly improved the performance of models. the reason for this is that although maltese is written in latin characters and contains italian loanwords, it remains a semitic language closely related to arabic. the proximity of comorian to arabic thus justifies the exploration of whether existing nlp solutions could be enhanced by adopting a similar approach. in the absence of work specifically addressing comorian written in arabic script, we present in ## the kamar-eddine system the standardization of comorian writing became a priority in the years following the independence of the comoros archipelago @xcite . while the idea of establishing specific rules for each dialect was quickly abandoned, the debate over whether to use the latin or arabic alphabet sparked intense discussions. on one hand, only a small minority of the population, educated in french, the colonial language, knew how to read the latin alphabet and thus advocated for its use. on the other hand, the majority, having received an education primarily in quranic schools, were proficient in reading the arabic alphabet. with public opinion in favor of the latter, arabic was quickly adopted for the translation of official documents. however, it is important to note that, despite the widespread use of this alphabet, there were no fixed rules governing its application. it was precisely in this context that ahmed kamar-eddine conceived the idea of standardizing this writing system. he began this project by publishing chronicles in his journal mwando (see the manuscript of the first edition in figure 1 ). title year description moroccan arabizi-to-arabic conversion using rule-based transliteration and weighted levenshtein algorithm (hajbi et al., 2024) 2024 it is a system of transliteration from arabizi (moroccan dialectal arabic written in latin characters) to arabic characters. the method used uses the levenshtein distance. exploring the impact of transliteration on nlp performance: treating maltese as an arabic dialect (micallef et al., 2023) 2023 improving the state of the art tal on several tasks by processing maltese written in arabic characters. a unified model for arabizi detection and transliteration using sequence-to-sequence models (shazal et al., 2020) 2020 pipeline for detecting arabizi in a text with code switches (arabic mixed with other languages, all written in latin characters) and transliteration into arabic characters. arabizi chat alphabet transliteration to algerian dialect (klouche and benslimane, 2020) 2020 transliteration into arabic characters of comments on the algerian telephone operator ooredoo in order to train a sentiment analysis model. ## methodology today, unless it has escaped our notice, there is no comorian database written in arabic script. to evaluate the effectiveness of our system, we are therefore compelled to rely solely on latin-script texts foot_0 as references. comprising 17,000 entries (sentences, words, and expressions), the dataset is first used to transliterate into arabic by applying the rules based on the constructed dictionary. we then perform reverse transliteration to recover the original text. to assess the quality of our system, we use word error rate (wer) and character error rate (cer) as metrics. the figure 2 summarizes the pipeline through which an input text passes during the inference of ## experimental results in this section, we first present the results and performance metrics of shialifube, along with descriptions of the various iterations adopted to improve its performance. additionally, we conduct an experiment on a real-world use case in speech recognition: the first machine learning model ever designed for comorian written in arabic script. convinced that open-source contributions are the key to advancing the representation of lowresource languages in the field of nlp, we have made the shialifube library foot_1 , its code on github foot_2 , and a huggingface space foot_3 publicly available for everyone. ## conclusion this work aimed to lay the foundation for nlp applied to the comorian language, with a focus on transcribing this language into arabic script using the kamar-eddine system. initially, we compiled the set of writing rules for this system, which served as the basis for shialifube, a bidirectional transliteration system for comorian. in the absence of parallel data to directly evaluate the performance of our solution, we adopted a round-trip transliteration approach. this involved transcribing a corpus from latin script to arabic script and then retranscribing it back to latin script. this method yielded promising metrics after several iterations: a wer of 14.84% and a cer of 9.56%. to assess the utility of this tool for practical use cases, we also conducted experiments in speech recognition. we observed encouraging performance with a wer of 37.44% for the arabic script version, although it remained slightly lower than the latin script model, which achieved a wer of 35.48%. finally, it is worth noting that this work represents a preliminary step. we plan to continue refining it as part of future contributions, hoping it will contribute to the preservation and enhancement of comorian intangible heritage. to encourage other researchers to further this initiative, we are making the entire source code, the shialifube library, and the trained models publicly available.