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    <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.</abstract>
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%0 Conference Proceedings
%T Human-Evaluated Urdu-English Speech Corpus: Advancing Speech-to-Text for Low-Resource Languages
%A Mehmood, Humaira
%A Abdul Rauf, Sadaf
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Anastasopoulos, Antonis
%S Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria (in-person and online)
%@ 979-8-89176-272-5
%F mehmood-abdul-rauf-2025-human
%X 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.
%R 10.18653/v1/2025.iwslt-1.12
%U https://aclanthology.org/2025.iwslt-1.12/
%U https://doi.org/10.18653/v1/2025.iwslt-1.12
%P 138-144

Markdown (Informal)

[Human-Evaluated Urdu-English Speech Corpus: Advancing Speech-to-Text for Low-Resource Languages](https://aclanthology.org/2025.iwslt-1.12/) (Mehmood & Abdul Rauf, IWSLT 2025)

• Human-Evaluated Urdu-English Speech Corpus: Advancing Speech-to-Text for Low-Resource Languages (Mehmood & Abdul Rauf, IWSLT 2025)

ACL

• Humaira Mehmood and Sadaf Abdul Rauf. 2025. Human-Evaluated Urdu-English Speech Corpus: Advancing Speech-to-Text for Low-Resource Languages. In Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025), pages 138–144, Vienna, Austria (in-person and online). Association for Computational Linguistics.