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@inproceedings{alhumid-etal-2026-abjad,
    title = "Abjad {AI} at {NADI} 2026 shared task: Overcoming Data Scarcity in {A}rabic Speech Processing through Augmentation",
    author = "Alhumid, Hatim  and
      Alharthi, Naif  and
      Alsaedi, Elaf S.  and
      Alasmary, Faris  and
      Ghannam, Ahmad  and
      Salamah, Anas Adel  and
      Sadah, Shouq  and
      Ghouti, Lahouari",
    editor = "Abdul-Mageed, Muhammad  and
      Elsayed, Tamer  and
      Bouamor, Houda  and
      El-Beltagy, Samhaa  and
      Al-Khalifa, Hend  and
      Abdelali, Ahmed  and
      Elmadany, AbdelRahim  and
      El Mekki, Abdellah  and
      Darwish, Kareem  and
      Alkhereyf, Sakhar  and
      Luqman, Hamzah  and
      Khalifa, Salam  and
      Magdy, Samar  and
      Alyafeai, Zaid  and
      Hamed, Injy  and
      Zaghouani, Wajdi  and
      Zitouni, Imed",
    booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.4/",
    pages = "47--52",
    ISBN = "979-8-89176-497-2",
    abstract = "We describe our submission to three NADI 2026 subtasks: Tunisian Arabic{--}French{--}English code-switched ASR (Task 1.3), dialectal Arabic speech translation, and 20-way Arabic dialect identification (ADI). For code-switched ASR, we finetune a 2B-parameter FastConformer encoder-decoder with synthetic Tunisian speech, cross-lingual data, waveform augmentation, and checkpoint averaging, achieving 14.12{\%} WER and ranking first overall. For speech translation, finetuning Whisper Large-v3 yields strong in-domain results but poor generalization (BLEU 5.8 on blind test), illustrating overfitting under limited data. For ADI, a classifier on frozen pretrained ASR representations outperforms full/partial finetuning, reaching 42{\%} accuracy across 20 dialects. We highlight synthetic augmentation, checkpoint averaging, and encoder freezing as effective low-resource strategies."
}

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    <abstract>We describe our submission to three NADI 2026 subtasks: Tunisian Arabic–French–English code-switched ASR (Task 1.3), dialectal Arabic speech translation, and 20-way Arabic dialect identification (ADI). For code-switched ASR, we finetune a 2B-parameter FastConformer encoder-decoder with synthetic Tunisian speech, cross-lingual data, waveform augmentation, and checkpoint averaging, achieving 14.12% WER and ranking first overall. For speech translation, finetuning Whisper Large-v3 yields strong in-domain results but poor generalization (BLEU 5.8 on blind test), illustrating overfitting under limited data. For ADI, a classifier on frozen pretrained ASR representations outperforms full/partial finetuning, reaching 42% accuracy across 20 dialects. We highlight synthetic augmentation, checkpoint averaging, and encoder freezing as effective low-resource strategies.</abstract>
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%0 Conference Proceedings
%T Abjad AI at NADI 2026 shared task: Overcoming Data Scarcity in Arabic Speech Processing through Augmentation
%A Alhumid, Hatim
%A Alharthi, Naif
%A Alsaedi, Elaf S.
%A Alasmary, Faris
%A Ghannam, Ahmad
%A Salamah, Anas Adel
%A Sadah, Shouq
%A Ghouti, Lahouari
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F alhumid-etal-2026-abjad
%X We describe our submission to three NADI 2026 subtasks: Tunisian Arabic–French–English code-switched ASR (Task 1.3), dialectal Arabic speech translation, and 20-way Arabic dialect identification (ADI). For code-switched ASR, we finetune a 2B-parameter FastConformer encoder-decoder with synthetic Tunisian speech, cross-lingual data, waveform augmentation, and checkpoint averaging, achieving 14.12% WER and ranking first overall. For speech translation, finetuning Whisper Large-v3 yields strong in-domain results but poor generalization (BLEU 5.8 on blind test), illustrating overfitting under limited data. For ADI, a classifier on frozen pretrained ASR representations outperforms full/partial finetuning, reaching 42% accuracy across 20 dialects. We highlight synthetic augmentation, checkpoint averaging, and encoder freezing as effective low-resource strategies.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.4/
%P 47-52

Markdown (Informal)

[Abjad AI at NADI 2026 shared task: Overcoming Data Scarcity in Arabic Speech Processing through Augmentation](https://aclanthology.org/2026.arabicnlp-sharedtasks.4/) (Alhumid et al., ArabicNLP 2026)

ACL

Hatim Alhumid, Naif Alharthi, Elaf S. Alsaedi, Faris Alasmary, Ahmad Ghannam, Anas Adel Salamah, Shouq Sadah, and Lahouari Ghouti. 2026. Abjad AI at NADI 2026 shared task: Overcoming Data Scarcity in Arabic Speech Processing through Augmentation. In Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks, pages 47–52, Budapest, Hungary. Association for Computational Linguistics.