SADSL y C : A Corpus for Saudi A rabian Multi-dialect Identification through Song Lyrics (2025)

Abstract: This paper presents the Saudi Arabian Dialects Song Lyrics Corpus (SADSLyC), the first dataset featuring song lyrics from the five major Saudi dialects: Najdi (Central Region), Hijazi (Western Region), Shamali (Northern Region), Janoubi (Southern Region), and Shargawi (Eastern Region). The dataset consists of 31,358 sentences, with each sentence representing a self-contained verse in a song, totaling 151,841 words. Additionally, we present a baseline experiment using the SaudiBERT model to classify the fine-grained dialects in the SADSLyC Corpus. The model achieved an overall accuracy of 73% on the test dataset.

## introduction through the analysis of arabic song lyrics, one can explore the rich linguistic nuances of the arabic language, recognise regional variations, and appreciate the artistic and literary elements present in the music. within the structure of a song, verses often serve as the storytelling components, unravelling the plot or message, while choruses provide a recurring, emotive anchor that reinforces the song's central theme 1 . the lyrics of arabic songs available online are categorised based on the singer's country of origin, regardless of the actual dialect of the lyrics. taking nancy ajram, a famous arabic singer, as an example foot_1 , despite frequently singing in the egyptian arabic dialect, she is consistently recognised as a lebanese singer. thus, whether her songs are in lebanese arabic or not, they are invariably placed within the list of lebanese songs on any musical platform @xcite . in this study, we do not rely on this classification to identify the saudi arabian dialect of the song lyrics. instead, we used the songwriter's region of origin to construct the sad-slyc corpus. we believe this approach is more accurate since it focuses on the lyrics written by the songwriter rather than the singer, who simply performs what is written. the sadslyc corpus consists of 1,892 saudi arabian songs, encompassing 31,358 sentences, and representing the five primary saudi arabian dialects: najdi, hijazi, shamali, janoubi,and shargawi. in this paper, we will use the terms sentence and verse interchangeably. the structure of this paper is as follows: section 2 outlines related works. section 3 describes the research methodology of this study. section 4 provides a description of the sadslyc corpus along with its statistical details. section 5 presents the baseline experiment and results. section 6 discusses the implications of the findings, addresses the limitations of the study. finally, section 7 provides a conclusion and suggestions for future work. ## related work the habibi corpus, developed by el-haj (2020), is currently the only arabic song lyrics dataset available in the literature. this corpus comprises 30,000 arabic songs from 18 countries, covering six arabic dialects: egyptian, levantine, gulf, maghrebi, iraqi, and sudanese. for dialect identification, the corpus was automatically labeled based on the nationality of the singer, providing a foundational resource for arabic dialectal analysis in song lyrics. however, none of the previously mentioned studies focus on fine-grained dialects of saudi arabia based on geographical location. in our previous work @xcite , we employed twitter for the arabic dialect identification task, using chatgpt for the identification process. we collected a small dataset from twitter using dialectal word lists, representing the five main dialects of saudi arabia: najdi, hijazi, shamali, janoubi, and shargawi. the sadslyc corpus stands distinct from existing literature due to its focus on a new genre, specifically saudi song lyrics. as noted by almuqren and cristea (2021), the majority of saudi corpora have primarily relied on twitter as the sole data source. ## methodology this section provides details on the construction of the sadslyc corpus, including the selection of songwriters, data collection, data preprocessing, and data labeling. ## corpus description the original song lyrics are parsed into sentences based on the verses. the finalized corpus is saved in json format. each song verse is assigned a unique "id" number, with the verse content stored under the "verse" field. verses belonging to the same song are associated with the same title, writer, and dialect. figures 1 , 2 ,3,4, and 5 show samples from the sadslyc corpus json files for najdi, hijazi, shamali, janoubi, and shargawi, respectively. the corpus is available 14 for academic and research purposes to enrich the development of arabic linguistic resources. the corpus statistics in ## discussion while the study assumes that songwriters from a given region use that region's dialect in their songs, this assumption may not always hold true. for instance, songwriters may prefer to write in modern standard arabic (msa) rather than their local dialect, such as shargawi. furthermore, song lyrics often incorporate multiple dialects as well as msa. these factors could introduce limitations to the study's assumption, as they may affect the regional representation in the corpus and restrict the findings related to dialect usage. a deep analysis of a subset of the sadslyc corpus, based on manual human annotation, reveals dialectal overlap across all dialects, particularly between najdi and hijazi. for example, the sentence , which translates to "we protect our country with the edge of the sword," is labeled as hijazi in sadslyc. however, this sentence could be labeled as both najdi and hijazi, as it lacks distinctive dialectal features. ## conclusion and future work to the best of our knowledge, there is currently no corpus specifically designed for saudi arabian song lyrics. the sadslyc corpus will be the first collection to feature saudi arabian songs, representing five major dialects spoken across the country. the experimental results highlight both the strengths and limitations of saudibert for dialect classification, particularly with respect to the finegrained saudi dialects, and underscore the need for further fine-tuning on more specialized datasets. as part of our future research, we plan to expand the sadslyc corpus by transcribing youtube videos that showcase a broader range of saudi songs and dialects.