@inproceedings{shoaib-etal-2026-resonate,
title = "Resonate at {NADI} 2026 Shared Task: Robust Speech Recognition and Dialect Identification under Acoustic and Domain Shift",
author = "Shoaib, Omar and
Sokar, Mohamed Ashraf and
Motawie, Mohamed and
AboElFottouh, Omar and
Hassouna, Ahmed and
Hassan, Al-Amir and
Zaki, Nader Essam and
Ali, Wael",
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.14/",
pages = "125--135",
ISBN = "979-8-89176-497-2",
abstract = "This paper describes the Resonate team{'}s submission to the NADI 2026 shared task on multidialectal Arabic speech processing. We participated in the robust dialectal Arabic ASR (Subtask 1.1) and spoken Arabic dialect identification (Task 2) subtasks, adding no external labelled speech. Our best single ASR model is a fully fine-tuned Cohere Transcribe Arabic, a 2.07B-parameter Arabic speech model, trained on 239.6K rows derived entirely from the 12.8K official clips through voice conversion, in-corpus noise augmentation and short-clip re-segmentation. Our submitted entry combines nine checkpoints of four sibling runs with a pivot-anchored ROVER vote and a per-dialect orthographic canonicaliser, obtaining 50.29{\%} WER and 24.43{\%} CER on the official test set and ranking second of nine teams. Our dialect identification system is a fine-tuned WavLM Base+ encoder with context-aware multi-head factorized attentive pooling under an angular-margin objective, which obtained 94{\%} accuracy on the validation set and 41{\%} on the out-of-domain test set. The ASR comparisons support augmenting scarce dialectal transcripts with acoustic variation, while ADI highlights the need for evaluation across domains. Code and reproducibility resources for both tasks are available at https://github.com/ResonateResearch/resonate-nadi-2026."
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<abstract>This paper describes the Resonate team’s submission to the NADI 2026 shared task on multidialectal Arabic speech processing. We participated in the robust dialectal Arabic ASR (Subtask 1.1) and spoken Arabic dialect identification (Task 2) subtasks, adding no external labelled speech. Our best single ASR model is a fully fine-tuned Cohere Transcribe Arabic, a 2.07B-parameter Arabic speech model, trained on 239.6K rows derived entirely from the 12.8K official clips through voice conversion, in-corpus noise augmentation and short-clip re-segmentation. Our submitted entry combines nine checkpoints of four sibling runs with a pivot-anchored ROVER vote and a per-dialect orthographic canonicaliser, obtaining 50.29% WER and 24.43% CER on the official test set and ranking second of nine teams. Our dialect identification system is a fine-tuned WavLM Base+ encoder with context-aware multi-head factorized attentive pooling under an angular-margin objective, which obtained 94% accuracy on the validation set and 41% on the out-of-domain test set. The ASR comparisons support augmenting scarce dialectal transcripts with acoustic variation, while ADI highlights the need for evaluation across domains. Code and reproducibility resources for both tasks are available at https://github.com/ResonateResearch/resonate-nadi-2026.</abstract>
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%0 Conference Proceedings
%T Resonate at NADI 2026 Shared Task: Robust Speech Recognition and Dialect Identification under Acoustic and Domain Shift
%A Shoaib, Omar
%A Sokar, Mohamed Ashraf
%A Motawie, Mohamed
%A AboElFottouh, Omar
%A Hassouna, Ahmed
%A Hassan, Al-Amir
%A Zaki, Nader Essam
%A Ali, Wael
%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 shoaib-etal-2026-resonate
%X This paper describes the Resonate team’s submission to the NADI 2026 shared task on multidialectal Arabic speech processing. We participated in the robust dialectal Arabic ASR (Subtask 1.1) and spoken Arabic dialect identification (Task 2) subtasks, adding no external labelled speech. Our best single ASR model is a fully fine-tuned Cohere Transcribe Arabic, a 2.07B-parameter Arabic speech model, trained on 239.6K rows derived entirely from the 12.8K official clips through voice conversion, in-corpus noise augmentation and short-clip re-segmentation. Our submitted entry combines nine checkpoints of four sibling runs with a pivot-anchored ROVER vote and a per-dialect orthographic canonicaliser, obtaining 50.29% WER and 24.43% CER on the official test set and ranking second of nine teams. Our dialect identification system is a fine-tuned WavLM Base+ encoder with context-aware multi-head factorized attentive pooling under an angular-margin objective, which obtained 94% accuracy on the validation set and 41% on the out-of-domain test set. The ASR comparisons support augmenting scarce dialectal transcripts with acoustic variation, while ADI highlights the need for evaluation across domains. Code and reproducibility resources for both tasks are available at https://github.com/ResonateResearch/resonate-nadi-2026.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.14/
%P 125-135
Markdown (Informal)
[Resonate at NADI 2026 Shared Task: Robust Speech Recognition and Dialect Identification under Acoustic and Domain Shift](https://aclanthology.org/2026.arabicnlp-sharedtasks.14/) (Shoaib et al., ArabicNLP 2026)
ACL
- Omar Shoaib, Mohamed Ashraf Sokar, Mohamed Motawie, Omar AboElFottouh, Ahmed Hassouna, Al-Amir Hassan, Nader Essam Zaki, and Wael Ali. 2026. Resonate at NADI 2026 Shared Task: Robust Speech Recognition and Dialect Identification under Acoustic and Domain Shift. In Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks, pages 125–135, Budapest, Hungary. Association for Computational Linguistics.