Elamur Research⌕
☰
home / papers / 2026 / English to Central Kurdish Speech Translation: Corpus Creation, Evaluation, and Orthographic Standardization

English to Central Kurdish Speech Translation: Corpus Creation, Evaluation, and Orthographic Standardization

Summary

Speech translation from English into Central Kurdish (CKB) has very little paired speech–text data. Inconsistent Kurdish spelling makes the problem worse. The authors build KUTED from TED and TEDx talks that volunteers, many at Koya University, translated into Kurdish. They collected captions from CaptionHub, matched them to the talk audio, and realigned the text at the sub-sentence and sentence levels. They then cut the audio into sentence-level segments. Human reviewers checked a sample of 1,000 pairs, and an ASR-based filter is proposed to catch misaligned segments.

Spelling variation in Kurdish turned out to lower BLEU noticeably. To fix this, the authors apply a three-stage standardization pipeline: AsoSoft normalization, a general correction table and a corpus-specific correction table. Fine-tuning Seamless on the standardized data gives clear gains. The authors also test a Seamless+NLLB cascade, a Fairseq Transformer trained from scratch, and NLLB for text-only translation.

Because of TED copyright, neither the audio nor the models can be shared. Only the list of talk IDs is published.

Key points

  • KUTED contains about 91k utterances from 1,696 talks (1,229 clean, 467 noisy), totalling 170 h of English audio, 1.65M English tokens and 1.40M CKB tokens.
  • In the 1,000-pair audit, 92.2% of segments were correctly aligned, and only 0.7% were clearly misaligned.
  • After standardization, fine-tuned Seamless improves from 11.34 to 15.18 BLEU on the KUTED test set, and it gains +3.0 BLEU over the baseline on FLEURS.
  • The cascade fine-tunes Seamless ASR, cutting WER from about 21 to 8.62, and NLLB-1.3B, raising BLEU from 9.25 to 15.57. The Fairseq Transformer trained from scratch reaches only 7.90 BLEU.
  • In text-only translation, fine-tuned NLLB scores 16.72 BLEU for EN→CKB and 27.93 BLEU for CKB→EN. The talk ID list is available on Hugging Face as aranemini/kutedlist.

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

Fields: Linguistics & languages · Machine translation

Topics: Machine translation

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4jy562hboezr

@inproceedings{mohammadamini2026english,
  title = {{English to Central Kurdish Speech Translation: Corpus Creation, Evaluation, and Orthographic Standardization}},
  author = {Mohammadamini, Mohammad and Jaff, Daban and Crego, Josep and Tahon, Marie and LAURENT, Antoine},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4jy562hboezr},
  url = {https://doi.org/10.63317/4jy562hboezr}
}

References

30 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Sina Ahmadi, Hossein Hassani, and Daban Q. Jaff. 2022. Leveraging multilingual news websites for building a kurdish parallel corpus. ACM Trans. Asian Low-Resour. Lang. Inf. Process., 21(5). ↗
  2. Zhila Amini, Mohammad Mohammadamini, Hawre Hosseini, Mehran Mansouri, and Daban Jaff. 2021. Central kurdish machine translation: First large scale parallel corpus and experiments. ↗
  3. Antonios Anastasopoulos, Alessandro Cattelan, Zi-Yi Dou, Marcello Federico, Christian Federmann, Dmitriy Genzel, Francisco Guzmán, Junjie Hu, Macduff Hughes, Philipp Koehn, Rosie Lazar, Will Lewis, Graham Neubig, Mengmeng Niu, Alp Öktem, Eric Paquin, Grace Tang, and Sylwia Tur. 2020. TICO-19: the translation initiative for COvid-19. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics. find
  4. Loïc Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia Gonzalez, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-jussà, Maha Elbayad, Hongyu Gong, Francisco Guzmán, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, and Mary Williamson. 2023. Seamless: Multilingual expressive and streaming speech translation. find our card
  5. Luisa Bentivogli, Mauro Cettolo, Marco Gaido, Alina Karakanta, Alberto Martinelli, Matteo Negri, and Marco Turchi. 2021. Cascade versus direct speech translation: Do the differences still make a difference? 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 2873–2887, Online. Association for Computational Linguistics. find
  6. Roldano Cattoni, Mattia Antonino, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2021. Mustc: A multilingual corpus for end-to-end speech translation. Computer Speech and Language, 66:101155. find
  7. Xi Chen, Songyang Zhang, Qibing Bai, Kai Chen, and Satoshi Nakamura. 2024. LLaST: Improved end-to-end speech translation system leveraged by large language models. In Findings of the Association for Computational Linguistics ACL 2024, pages 6976–6987, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics. find
  8. 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. find our card
  9. Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. 2022. No language left behind: Scaling human-centered machine translation. find
  10. Mattia A. Di Gangi, Roldano Cattoni, Luisa Bentivogli, Matteo Negri, and Marco Turchi. 2019. Must-c: A multilingual speech translation corpus. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2012–2017, Minneapolis, Minnesota. Association for Computational Linguistics. find our card
  11. Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’ Aurelion Ranzato, Francisco Guzman, 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. ↗
  12. Amir Hassanpour. 1992. Nationalism and Language in Kurdistan, 1918-1985. San Francisco, CA: Mellen Research University Press. find
  13. Ye Jia, Ron J. Weiss, Fadi Biadsy, Wolfgang Macherey, Melvin Johnson, Zhifeng Chen, and Yonghui Wu. 2019. Direct Speech-to-Speech Translation with a Sequence-to-Sequence Model. In Proc. Interspeech 2019, pages 1123–1127. find
  14. Dan Jurafsky and James H. Martin. 2009. Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Pearson Prentice Hall. find
  15. Ali Can Kocabiyikoglu, Laurent Besacier, and Olivier Kraif. 2018. Augmenting librispeech with french translations: A multimodal corpus for direct speech translation evaluation. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). find
  16. Kurdish-Academy. 2010. Rasipardekanî konfransî berew rênûsêkî yekgrtûy kurdî. The Journal of Kurdish Academy (In Kurdish). find
  17. Aso Mahmudi, Hadi Veisi, Mohammad MohammadAmini, and Hawre Hosseini. 2019. Automated kurdish text normalization. find
  18. Mohammad Mohammadamini, Daban Jaff, Sara Jamal, Ibrahim Ahmed, Hawkar Omar, Darya Sabr, Marie Tahon, and Antoine Laurent. 2025a. Kuvost: A large-scale human-annotated English to Central Kurdish speech translation dataset driven from English common voice. In Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025), pages 106–109, Vienna, Austria (in-person and online). Association for Computational Linguistics. find
  19. Mohammad Mohammadamini, Aghilas Sini, Marie Tahon, and Antoine Laurent. 2025b. Scaling pseudo-labeling data for end-to-end low-resource speech translation (the case of Kurdish language). In Interspeech 2025, pages 898–902. find
  20. Elizabeth Salesky, Matthew Wiesner, Jacob Bremerman, Roldano Cattoni, Matteo Negri, Marco Turchi, Douglas W. Oard, and Matt Post. 2021. Multilingual tedx corpus for speech recognition and translation. In Proceedings of Interspeech. find
  21. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715–1725, Berlin, Germany. Association for Computational Linguistics. find
  22. Nivedita Sethiya and Chandresh Kumar Maurya. 2024. End-to-end speech-to-text translation: A survey. ↗
  23. Nivedita Sethiya, Saanvi Nair, and Chandresh Maurya. 2024. Indic-tedst: Datasets and baselines for low-resource speech to text translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9019–9024, Torino, Italia. ELRA and ICCL. find
  24. Jaffer Sheyholislami. 2015. The Kurds: History, Religion, Language, Politics, chapter Language Varieties of the Kurds. Austrian Federal Ministry of the Interior. find
  25. Hadi Veisi, Hawre Hosseini, Mohammad MohammadAmini, Wirya Fathy, and Aso Mahmudi. 2022. Jira: a central kurdish speech recognition system, designing and building speech corpus and pronunciation lexicon. Lang. Resour. Eval., 56(3):917–941. ↗
  26. Hadi Veisi, Mohammad MohammadAmini, and Hawre Hosseini. 2019. Toward Kurdish language processing: Experiments in collecting and processing the AsoSoft text corpus. Digital Scholarship in the Humanities, 35(1):176–193. ↗
  27. Changhan Wang, Juan Pino, Anne Wu, and Jiatao Gu. 2020a. Covost: A diverse multilingual speech-to-text translation corpus. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4197–4203, Marseille, France. European Language Resources Association. ↗
  28. Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, and Emmanuel Dupoux. 2021a. Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. 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 993–1003, Online. Association for Computational Linguistics. ↗ our card
  29. Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, and Juan Pino. 2020b. Fairseq S2T: Fast speech-to-text modeling with fairseq. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations, pages 33–39, Suzhou, China. Association for Computational Linguistics. ↗
  30. Changhan Wang, Anne Wu, Jiatao Gu, and Juan Pino. 2021b. Covost 2 and massively multilingual speech translation. In Proc. Interspeech 2021, pages 2247–2251. find

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/mohammadamini-2026-english-central-kurdish-speech-translation/