@inproceedings{mehmood-abdul-rauf-2025-human,
    title = "Human-Evaluated {U}rdu-{E}nglish Speech Corpus: Advancing Speech-to-Text for Low-Resource Languages",
    author = "Mehmood, Humaira  and
      Abdul Rauf, Sadaf",
    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-1.12/",
    pages = "138--144",
    ISBN = "979-8-89176-272-5",
    abstract = "This paper presents our contribution to the IWSLT Low Resource Track 2: `Training and Evaluation Data Track'. We share a human-evaluated Urdu-English speech-to-text corpus based on Common Voice 13.0 Urdu speech corpus. We followed a three-tier validation scheme which involves an initial automatic translation with corrections from native reviewers, full review by evaluators followed by final validation from a bilingual expert ensuring reliable corpus for subsequent NLP tasks. Our contribution, CV-UrEnST corpus, enriches Urdu speech resources by contributing the first Urdu-English speech-to-text corpus. When evaluated with Whisper-medium, the corpus yielded a significant improvement to the vanilla model in terms of BLEU, chrF++, and COMET scores, demonstrating its effectiveness for speech translation tasks."
}