BUL, a multi-dialect Arabic ASR dataset collected from 275 speakers in 11 Arab countries, includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents to support accent-aware modeling.
Abstract
Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited speech resources. Existing speech datasets often focus on single dialects or large-scale broadcast/web data, leading to trade-offs between linguistic diversity and annotation quality. We present BULBUL, a multi-dialect Arabic ASR dataset collected from 275 speakers in 11 Arab countries. BULBUL includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents to support accent-aware modeling. The quality of the recordings was ensured through a two-level human verification process. We further benchmark a range of recent ASR systems, establishing strong baselines for modern dialectal and accented Arabic ASR.
Arabizi refers to Arabic written in Latin script. Although previous studies have shown that the prevalence and usage of Arabizi vary by factors such as region and age group, most NLP research on Arabic texts treats it as a temporary phenomenon resulting from limited technological support for the Arabic script. In this work, we engage with Arabic speakers to collect insights on their perceptions and usage of Arabizi. We further examine writing norms among speakers of different dialects, focusing on Algerian, Egyptian, Lebanese, Moroccan, and Tunisian Arabic. To this end, we release two resources. First, a character-level alignment of Arabic words to study inter- and intra-dialectal variation across these five dialects, based on words transliterated by survey participants, finding systematic intra-dialectal regularity and inter-dialectal variation. Second, to study Arabic speakers'ability to identify this stylistic variation at the sentence-level, we build a manually curated parallel corpus of sentences written in Arabic script alongside multiple Arabizi transliterations, collected from speakers of the same five dialects. Our study presents the largest human-centered, cross-dialectal study of Arabizi's perceptions and practices to date.
Amr Keleg, Ahmed Amine Ben Abdallah, Taha Yassine et al.· 0 citations
With the growing use of social networks and online platforms, hate speech and racist content detection is becoming increasingly challenging, especially in the Arab world, which is characterized by a significant diversity of complex dialects. This article highlights the importance of implementing detection systems capable of identifying hate speech, based on a critical analysis of scientific literature published between 2020 and 2025. This paper highlights the diversity of Arabic dialects and their linguistic complexity, which make them difficult to analyze using a single model. As a result, the utility of using multimodal approaches for detecting hate speech is emphasized. The article presents this approach and evaluates the effectiveness of several artificial intelligence-based methods, implementing methodological innovations and identifying gaps in current research, as well as addressing key challenges such as the limited consideration of Maghrebi dialects, the lack of datasets for these dialects, especially Moroccan Darija.
M. Hassane, M. Azrour, A. Hessane et al.· Mathematical Modeling and Co...· 0 citations
Automatic speech recognition (ASR) systems perform strongly on native-English benchmarks, yet their accuracy degrades sharply when the input speech comes from under represented non-native accents. Nepali-accented English is particularly under-served: existing resources either focus on native Nepali speech, cover broader multi-accent settings without dedicated Nepali evaluation, or provide only limited Nepali-accent coverage. This paper presents a Nepali-accented English evaluation dataset designed to support robust ASR benchmarking under accent mismatch. The corpus was collected through a web-based platform that did not collect directly identifying metadata and contains recordings from 57 speakers. Each session follows a fixed 22-prompt protocol consisting of 11 phonetic prompts, 10 domain prompts, and 1 spontaneous prompt, providing complementary coverage of pronunciation, topical vocabulary, and natural speaking style. In addition to transcribed speech, the dataset includes participant metadata for coarse exploratory subgroup analysis and speaker-level manual recording-quality labels. Manual quality assessment shows that 50.9% of sessions are clean and 42.1% contain only mild noise. As a descriptive reference, open-source ASR baselines are substantially worse on this corpus than the corresponding LibriSpeech test-clean values reported in official model cards, reaching 38.15–55.00% WER on the collected set versus reported 2–4% WER on LibriSpeech test-clean. These baseline results position the corpus as a practical held-out resource for evaluating accent robustness and out-of-distribution generalization on Nepali-accented English.
Santosh Dahal, K. Dahal· Everest Advances in Science...· 0 citations
Dialectal Arabic (DA) remains under-resourced compared to Modern Standard Arabic (MSA), particularly for machine reading comprehension (MRC) and question answering (QA). Existing Arabic QA benchmarks primarily focus on formal written MSA or multiple-choice QA, with limited coverage of naturally spoken dialects. Here, we aim to bridge this gap. We introduce EDRAC, the first large-scale benchmark for dialectal Arabic machine reading comprehension (MRC) and generative QA, covering five major dialects: Egyptian, Moroccan, Emirati, Syrian, and Saudi Arabic. EDRAC contains 499 passages derived from naturally occurring spoken interactions and 4,977 corresponding QA pairs generated through a human--LLM collaborative pipeline combining iterative generation, LLM-as-a-judge evaluation, and human verification. We benchmark Arabic-centric and multilingual LLMs on EDRAC using lexical and semantic metrics. Our results reveal substantial gaps between semantic answer quality and dialectal fidelity, highlighting the limitations of existing evaluation metrics for dialectal Arabic generation. EDRAC provides a realistic and challenging MRC benchmark for future research on dialectal Arabic NLP.
Noor Abo Mokh, K. Chirkunov, Teresa Lynn et al.· 0 citations
In this work, we introduce Indic DiarBench, a speaker diarization and ASR benchmark dataset spanning all 22 scheduled languages of India. This corpus comprises approximately 108 hours of natural multi-speaker audio from near-field meetings, far-field recordings, and in-the-wild audios. All annotations are human-corrected with time-aligned speaker attributed transcriptions. The dataset captures conversational nuance prevalent in Indian speech, such as English code-mixing, dialectal variation, and frequent speaker overlap. To establish a baseline for joint ASR and diarization capabilities we evaluate leading systems including commercial speech APIs and multimodal large language models. Indic DiarBench is released as an open-access resource to advance inclusive, multilingual speech technology research for Indian languages.
