Language and Speech Technology for Central Kurdish Varieties
Sina Ahmadi, Daban Jaff, Md Mahfuz Ibn Alam, Antonios Anastasopoulos
Abstract
Kurdish, an Indo-European language spoken by over 30 million speakers, is considered a dialect continuum and known for its diversity in language varieties. Previous studies addressing language and speech technology for Kurdish handle it in a monolithic way as a macro-language, resulting in disparities for dialects and varieties for which there are few resources and tools available. In this paper, we take a step towards developing resources for language and speech technology for varieties of Central Kurdish, creating a corpus by transcribing movies and TV series as an alternative to fieldwork. Additionally, we report the performance of machine translation, automatic speech recognition, and language identification as downstream tasks evaluated on Central Kurdish subdialects. Data and models are publicly available under an open license at https://github.com/sinaahmadi/CORDI.- Anthology ID:
- 2024.lrec-main.877
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 10034–10045
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.877
- DOI:
- Cite (ACL):
- Sina Ahmadi, Daban Jaff, Md Mahfuz Ibn Alam, and Antonios Anastasopoulos. 2024. Language and Speech Technology for Central Kurdish Varieties. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10034–10045, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Language and Speech Technology for Central Kurdish Varieties (Ahmadi et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.lrec-main.877.pdf