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Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology

Paper · 2026 · Peter Sullivan, AbdelRahim A. Elmadany, Alcides Alcoba Inciarte et al.

Summary

Speech resources for Dialectal Arabic (DA) are scattered and inconsistent. Corpora use different dialect labeling schemes (country names, broad regions, ISO codes), cover different domains, and are recorded under different conditions. This makes it hard to compare datasets or evaluate models fairly. The authors address this with Arab Voices, a framework that collects public and licensed DA corpora and converts them into one shared format. Dialect metadata is harmonized by pairing ISO 639-3 codes with country-region tags, and domain and recording-style labels are unified. Each training split is then profiled along two axes: text-based dialectness, estimated with ALDi and spot-checked by humans, and objective proxies of audio quality. The profiling shows large differences between corpora in acoustic conditions and in how strong and consistent the dialectal signal is. Coarse labels alone therefore say little about a dataset. On top of this harmonized data, the authors build a multi-dialect ASR benchmark and test recent open-weight systems, from speech-specific models to audio-capable multimodal LLMs. The result is reproducible baselines and a practical guide to where new data collection is most needed.

Key points

  • Arab Voices unifies 31 datasets covering 14 dialects. Examples include MGB2/MGB3/MGB5, QASR, CALLHOME, FLEURS, Common Voice 22, MASC and Casablanca.
  • Dialect labels pair ISO 639-3 codes with country-region tags, which gives finer groupings than country-only labels. The figure also mentions standardized benchmark sets of about one hour per ISO code.
  • Utterances with poor predicted quality tend to have higher WER. This relationship does not hold for the subjective non-matching-reference quality estimator.
  • The Iraqi varieties (Mesopotamian, North Mesopotamian) and those spoken across North Africa (Moroccan, Algerian, Tunisian, Hassaniyya) are still the weakest and least supported. Some dialects had no qualifying datasets at all.
  • Stated limitations include:
    • ALDi works only on text and cannot capture pronunciation, so the authors propose a "spoken ALDi" as future work.
    • Evaluation uses a single reference transcript, which handles flexible DA spelling poorly.
    • ISO codes are only an imperfect proxy for the dialect continuum.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of British ColumbiaVancouver · Canada98 works · 34,805 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSignal Processing · 452 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 2,908 works

Cite

Cite as Peter Sullivan, AbdelRahim A. Elmadany, Alcides Alcoba Inciarte and Muhammad Abdul-Mageed. 2026. Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.575

@inproceedings{sullivan2026arab,
  title = {{Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology}},
  author = {Peter Sullivan, AbdelRahim A. Elmadany, Alcides Alcoba Inciarte and Muhammad Abdul-Mageed},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.575},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.575}
}

References

71 references, 22 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Muhammad Abdul-Mageed, Abdelrahim Elmadany, and 1 others. 2021. Arbert & marbert: Deep bidirectional transformers for arabic. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7088–7105.
  2. Badr M. Abdullah, Matthew Baas, Bernd Möbius, and Dietrich Klakow. 2025. Voice Conversion Improves Cross-Domain Robustness for Spoken Arabic Dialect Identification. In Interspeech 2025, pages 2790– 2794.
  3. Idris Abdulmumin, Victor Agostinelli, Tanel Alumäe, Antonios Anastasopoulos, Luisa Bentivogli, Ondřej Bojar, Claudia Borg, Fethi Bougares, Roldano Cattoni, Mauro Cettolo, Lizhong Chen, William Chen, Raj Dabre, Yannick Estève, Marcello Federico, Mark Fishel, Marco Gaido, Dávid Javorský, Marek Kasztelnik, and 33 others. 2025. Findings of the IWSLT 2025 evaluation campaign. In Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025), pages 412–481, Vienna, Austria (in-person and online). Association for Computational Linguistics. find
  4. Maryam Al Ali and Hanan Aldarmaki. 2024. Mixat: A data set of bilingual emirati-english speech. In SIGUL 2024: 3rd Annual Meeting of the ELRA/ISCA Special Interest Group on Under-resourced Languages, a Satellite Workshop of LREC-COLING 2024. ↗
  5. Mohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, and Maha Dawas. 2023. Masc: Massive arabic speech corpus. In 2022 IEEE Spoken Language Technology Workshop (SLT), pages 1006–1013. IEEE. find
  6. Abdulkafi Albirini. 2011. The sociolinguistic functions of codeswitching between standard arabic and dialectal arabic. Language in society, 40(5):537–562. Statement of Generative AI use We used Gemini to customize initial data preprocessing scripts for each of the different datasets. These were later edited and manually checked for correct preprocessing output. Sadeen Alharbi, Areeb Alowisheq, Zoltán Tüske, Kareem Darwish, Abdullah Alrajeh, Abdulmajeed Al-rowithi, Aljawharah Bin Tamran, Asma Ibrahim, https://alliancecan.ca https://arc.ubc.ca/ubc-arc-sockeye ↗
  7. Raghad Aloraini, Raneem Alnajim, and 1 others. 2024. Sada: Saudi audio dataset for arabic. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 10286–10290. IEEE. find
  8. Ahmed Ali, Peter Bell, James Glass, Yacine Messaoui, Hamdy Mubarak, Steve Renals, and Yifan Zhang. 2016. The mgb-2 challenge: Arabic multi-dialect broadcast media recognition. In 2016 IEEE Spoken Language Technology Workshop (SLT), pages 279– 284. IEEE.
  9. Ahmed Ali, Suwon Shon, Younes Samih, Hamdy Mubarak, Ahmed Abdelali, James Glass, Steve Renals, and Khalid Choukri. 2019. The mgb-5 challenge: Recognition and dialect identification of dialectal arabic speech. In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pages 1026–1033. IEEE. find
  10. Ahmed Ali, Stephan Vogel, and Steve Renals. 2017. Speech recognition challenge in the wild: Arabic mgb-3. In 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pages 316– 322. IEEE.
  11. Ammar Mohammed Ali Alqadasi, Rawad Abdulghafor, Mohd Shahrizal Sunar, and Md Sah Bin HJ Salam. 2023. Modern standard arabic speech corpora: A systematic review. Ieee Access, 11:55771–55796. ↗
  12. Ammar Mohammed Ali Alqadasi, Akram M Zeki, Mohd Shahrizal Sunar, Siti Zaiton Mohd Hashim, Md Sah hj Salam, and Rawad Abdulghafor. 2025. Arabic dialects speech corpora: A systematic review. Speech Communication, page 103322. find
  13. Norah Alrashoudi, Hend AlKhalifa, and Yousef Ajami Alotaibi. 2024. L2-ksu native and non-native arabic speech. https://doi.org/10.35111/n3d8-t960. LDC2024S11. ↗
  14. Hamzah A Alsayadi, Abdelaziz A Abdelhamid, Islam Hegazy, Bandar Alotaibi, and Zaki T Fayed. 2022. Deep investigation of the recent advances in dialectal arabic speech recognition. IEEE access, 10:57063– 57079. ↗
  15. Eiman Alsharhan and Allan Ramsay. 2020. Investigating the effects of gender, dialect, and training size on the performance of arabic speech recognition. Language Resources and Evaluation, 54(4):975–998. ↗
  16. Appen Pty Ltd. 2006a. Gulf arabic conversational telephone speech. https://doi.org/10.35111/ nsvg-dd69. LDC2006S43. ↗
  17. Appen Pty Ltd. 2006b. Iraqi arabic conversational telephone speech. https://doi.org/10.35111/ 2dcs-9751. LDC2006S45. ↗
  18. Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, and Gregor Weber. 2019. Common voice: A massively-multilingual speech corpus. arXiv preprint arXiv:1912.06670. ↗
  19. Loïc Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, and 1 others. 2023. Seamless: Multilingual expressive and streaming speech translation. arXiv preprint arXiv:2312.05187. ↗
  20. Mourad Belhadj, Ilham Bendellali, and Elalia Lakhdari. 2023. Kasdi-merbah university emotional database in arabic speech. https://doi.org/10.35111/ qqer-qz15. LDC2023S10. ↗
  21. Emily M Bender and Batya Friedman. 2018. Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6:587–604. ↗
  22. Alexandra Canavan, George Zipperlen, and David Graff. 1997. Callhome egyptian arabic speech. https://doi.org/10.35111/d8yb-9m13. LDC97S45. ↗
  23. Khansa Chemnad and Achraf Othman. 2023. Advancements in arabic text-to-speech systems: a 22-year literature review. IEEE Access, 11:30929–30954. ↗
  24. Alexis Conneau, Min Ma, Simran Khanuja, Yu Zhang, Vera Axelrod, Siddharth Dalmia, Jason Riesa, Clara Rivera, and Ankur Bapna. 2023. Fleurs: Few-shot learning evaluation of universal representations of speech. In 2022 IEEE Spoken Language Technology Workshop (SLT), pages 798–805. IEEE.
  25. David M. Eberhard, Gary F. Simons, and Charles D. Fennig. 2025. Ethnologue: Languages of the world. find
  26. Penelope Eckert. 2016. Variation, meaning and social change, page 68–85. Cambridge University Press. find
  27. Ashraf Elnagar, Sane M Yagi, Ali Bou Nassif, Ismail Shahin, and Said A Salloum. 2021. Systematic literature review of dialectal arabic: identification and detection. IEEE Access, 9:31010–31042. ↗
  28. Alexandre Ferro Filho, Diogo Fernandes Costa Silva, Pedro Elias Engelberg Silva Borges, and Arlindo Rodrigues Galvão Filho. 2025. Evaluating Deep Speaker Embedding Robustness to Domain, Sampling Rate, and Codec Variations. In Interspeech 2025, pages 1113–1117. find
  29. Mahault Garnerin, Solange Rossato, and Laurent Besacier. 2021. Investigating the Impact of Gender Representation in ASR Training Data: A Case Study on Librispeech. In Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing, pages 86–92. Association for Computational Linguistics. find
  30. Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. 2022. The flores-101 evaluation benchmark for low-resource and multilingual machine translation. Transactions of the Association for Computational Linguistics, 10:522–538. ↗
  31. Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, and Ruoming Pang. 2020. Conformer: Convolution-augmented transformer for speech recognition. In Interspeech 2020, pages 5036–5040.
  32. Nizar Habash and David Palfreyman. 2022. Zaebuc: An annotated arabic-english bilingual writer corpus. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 79–88. find
  33. Nawar Halabi. 2016. Modern standard Arabic phonetics for speech synthesis. Ph.D. thesis, University of Southampton. find
  34. Injy Hamed, Fadhl Eryani, David Palfreyman, and Nizar Habash. 2024. ZAEBUC-spoken: A multilingual multidialectal Arabic-English speech corpus. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17770– 17782, Torino, Italia. ELRA and ICCL.
  35. Injy Hamed, Ngoc Thang Vu, and Slim Abdennadher. 2020. Arzen: A speech corpus for code-switched egyptian arabic-english. In Proceedings of the twelfth language resources and evaluation conference, pages 4237–4246. find
  36. Anurag Kumar, Ke Tan, Zhaoheng Ni, Pranay Manocha, Xiaohui Zhang, Ethan Henderson, and Buye Xu. 2023. Torchaudio-squim: Reference-less speech quality and intelligibility measures in torchaudio. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE. find
  37. Jonathan Le Roux, Scott Wisdom, Hakan Erdogan, and John R Hershey. 2019. Sdr–half-baked or well done? In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 626–630. IEEE. ↗
  38. Nala H Lee. 2025. Sociolinguistic variation, chapter 2. Routledge. find
  39. Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Paden Tomasello, Jacob Kahn, Gilad Avidov, Ronan Collobert, and Gabriel Synnaeve. 2020. Rethinking evaluation in asr: Are our models robust enough? arXiv preprint arXiv:2010.11745. MagicData. no date(a). Asr-egarbcsc: An egyptian arabic conversational speech corpus. MagicData. no date(b). Asr-egarbcsc: An egyptian arabic conversational speech corpus. ↗
  40. Nourhan Mahmoud. 2025. Arabic-diacritized-tts. find
  41. Judith T. Irvine. 2001. “Style” as distinctiveness: the culture and ideology of linguistic differentiation, chapter 1. Cambridge University Press. find
  42. Salima Mdhaffar, Fethi Bougares, Renato De Mori, Salah Zaiem, Mirco Ravanelli, and Yannick Estève. 2024. Taric-slu: A tunisian benchmark dataset for spoken language understanding. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15606–15616. find
  43. Amr Keleg, Sharon Goldwater, and Walid Magdy. 2023. Aldi: Quantifying the arabic level of dialectness of text. arXiv preprint arXiv:2310.13747. ↗
  44. Ali Hamid Meftah, Yousef Ajami Alotaibi, and Sid-Ahmed Selouani. 2017. Ksuemotions. https://doi.org/10.35111/q1eh-6457. LDC2017S12. ↗
  45. Hayder Kharrufa, Adam Taha, and Mohammed Baraq. 2024. Training a Text-to-Speech System for Dialectal Arabic with a Focus on the Iraqi Dialect. find
  46. Hamdy Mubarak, Amir Hussein, Shammur Absar Chowdhury, and Ahmed Ali. 2021. Qasr: Qcri aljazeera speech resource–a large scale annotated arabic speech corpus. arXiv preprint arXiv:2106.13000. ↗
  47. Yuri Khokhlov, Tatiana Prisyach, Anton Mitrofanov, Dmitry Dutov, Igor Agafonov, Tatiana Timofeeva, Aleksei Romanenko, and Maxim Korenevsky. 2024. Classification of Room Impulse Responses and its application for channel verification and diarization. In Interspeech 2024, pages 3250–3254. find
  48. Gautham J Mysore. 2014. Can we automatically transform speech recorded on common consumer devices in real-world environments into professional production quality speech?—a dataset, insights, and challenges. IEEE Signal Processing Letters, 22(8):1006– 1010. ↗
  49. Rostislav Kolobov, Olga Okhapkina, Andrey Platunov Olga Omelchishina, Roman Bedyakin, Vyacheslav Moshkin, Dmitry Menshikov, and Nikolay Mikhaylovskiy. 2021. Mediaspeech: Multi-language asr benchmark and dataset. Preprint, arXiv:2103.16193. ↗
  50. Hedi Naouara, Jean-Pierre Lorré, and Jérôme Louradour. 2025. Linto audio and textual datasets to train and evaluate automatic speech recognition in tunisian arabic dialect. In Workshop on Preparing Good Data for Generative AI: Challenges and Approaches. ↗
  51. Ajinkya Kulkarni, Atharva Kulkarni, Sara Abedal-mon’em Mohammad Shatnawi, and Hanan Aldarmaki. 2023. Clartts: An open-source classical arabic text-to-speech corpus. In 2023 INTERSPEECH, pages 5511–5515.
  52. ASR Omnilingual, Gil Keren, Artyom Kozhevnikov, Yen Meng, Christophe Ropers, Matthew Setzler, Skyler Wang, Ife Adebara, Michael Auli, Can Balioglu, and 1 others. 2025. Omnilingual asr: Open-source multilingual speech recognition for 1600+ languages. arXiv preprint arXiv:2511.09690. OpenSLR. Mohammed - Quran Speech to Text Dataset. https://www.openslr.org/132/. ↗
  53. OpenSLR. 2003. https://www.openslr.org/46/. Tunisian_MSA. ↗
  54. Pranay Manocha and Anurag Kumar. 2022. Speech Quality Assessment through MOS using Non-Matching References. In Interspeech 2022, pages 654–658. find
  55. Pranay Manocha and Zeyu Jin and Adam Finkelstein. 2022. Audio Similarity is Unreliable as a Proxy for Audio Quality. In Interspeech 2022, pages 3553– 3557. find
  56. Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, and 1 others. 2024. Scaling speech technology to 1,000+ languages. Journal of Machine Learning Research, 25(97):1–52.
  57. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. 2023. Robust speech recognition via large-scale weak supervision. In International conference on machine learning, pages 28492–28518. PMLR. ↗
  58. Antony W Rix, John G Beerends, Michael P Hollier, and Andries P Hekstra. 2001. Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs. In 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. No. 01CH37221), volume 2, pages 749–752. IEEE. find
  59. Marshima Mohd Rosli, Ewan Tempero, and Andrew Luxton-Reilly. 2018. Evaluating the quality of datasets in software engineering. Advanced Science Letters, 24(10):7232–7239. ↗
  60. Myeonghoon Ryu, Hongseok Oh, Suji Lee, and Han Park. 2025. Unified Microphone Conversion: Many-to-Many Device Mapping via Feature-wise Linear Modulation. In Interspeech 2025, pages 1333–1337. find
  61. Cees H Taal, Richard C Hendriks, Richard Heusdens, and Jesper Jensen. 2011. An algorithm for intelligibility prediction of time–frequency weighted noisy speech. IEEE Transactions on audio, speech, and language processing, 19(7):2125–2136. ↗
  62. Bashar Talafha, Karima Kadaoui, Samar Mohamed Magdy, Mariem Habiboullah, Chafei Mohamed Chafei, Ahmed Oumar El-Shangiti, Hiba Zayed, Rahaf Alhamouri, Rwaa Assi, Aisha Alraeesi, and 1 others. 2024. Casablanca: Data and models for multidialectal arabic speech recognition. arXiv preprint arXiv:2410.04527. ↗
  63. Hawau Olamide Toyin, Rufael Marew, Humaid Al-blooshi, Samar M. Magdy, and Hanan Aldarmaki. 2025. Arvoice: A multi-speaker dataset for arabic speech synthesis. Preprint, arXiv:2505.20506. ↗
  64. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30.
  65. Kevin Walker, Christopher Caruso, Kazuaki Maeda, Denise DiPersio, and Stephanie Strassel. 2013. Gale phase 2 arabic broadcast conversation speech part 1. https://doi.org/10.35111/j224-tx54. LDC2013S07. ↗
  66. Ronald Wardhaugh and Janet M Fuller. 2021. An introduction to sociolinguistics. John Wiley & Sons. find
  67. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388. ↗
  68. Omar Zaidan and Chris Callison-Burch. 2011. The arabic online commentary dataset: an annotated dataset of informal arabic with high dialectal content. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 37–41.
  69. SDAIA. 2022. Saudilang code-switch corpus (scc). https://www.kaggle.com/datasets/sdaiancai/ saudilang-code-switch-corpus-scc. CC BY-NC-SA 4.0. ↗
  70. Hagen Soltau, Lidia Mangu, and Fadi Biadsy. 2011. From modern standard arabic to levantine asr: Leveraging gale for dialects. In 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, pages 266–271. IEEE. find
  71. Peter Sullivan, AbdelRahim Elmadany, and Muhammad Abdul-Mageed. 2023. On the robustness of arabic speech dialect identification. In Interspeech 2023, pages 5326–5330.

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