@inproceedings{mohammadamini-etal-2025-kuvost,
    title = "Kuvost: A Large-Scale Human-Annotated {E}nglish to {C}entral {K}urdish Speech Translation Dataset Driven from {E}nglish Common Voice",
    author = "Mohammadamini, Mohammad  and
      Jaff, Daban  and
      Jamal, Sara  and
      Ahmed, Ibrahim  and
      Omar, Hawkar  and
      Sabr, Darya  and
      Tahon, Marie  and
      Laurent, Antoine",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.iwslt-1.9/",
    pages = "106--109",
    ISBN = "979-8-89176-272-5",
    abstract = "In this paper, we introduce the Kuvost, a large-scale English to Central Kurdish speech-to-text-translation (S2TT) dataset. This dataset includes 786k utterances derived from Common Voice 18, translated and revised by 230 volunteers into Central Kurdish. Encompassing 1,003 hours of translated speech, this dataset can play a groundbreaking role for Central Kurdish, which severely lacks public-domain resources for speech translation. Following the dataset division in Common Voice, there are 298k, 6,226, and 7,253 samples in the train, development, and test sets, respectively. The dataset is evaluated on end-to-end English-to-Kurdish S2TT using Whisper V3 Large and SeamlessM4T V2 Large models. The dataset is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License https://huggingface.co/datasets/aranemini/kuvost."
}