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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.17846 |
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| _version_ | 1866913139983908864 |
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| author | Haq, Attia Nafees ul Zhu, Zeyu Hu, Jingbin He, ChunJiang Xie, Lei |
| author_facet | Haq, Attia Nafees ul Zhu, Zeyu Hu, Jingbin He, ChunJiang Xie, Lei |
| contents | Despite 230 million speakers, Urdu remains critically under-resourced in speech technology. We introduce UrduSpeech: a large high-fidelity Urdu corpus comprising 156 hours of audio with 12-dimension paralinguistic metadata, encompassing US-Std, US-CS, US-EngPk. To address Right-to-Left script constraints and frequent code-switching, we developed UrduSpeech, a LLM-driven pipeline to curate data across 12 diverse categories, including news, drama, and rare literary forms like Bait-Bazi. We also release a 9-hour US-Benchmark set, manually corrected by native annotators to serve as a standard. Human quality assessment of the primary 156-hour corpus yielded a Mean Opinion Score (MOS) of 4.6 (std = 0.7) with inter-rater reliability confirmed by a 0.68 Cohen's Kappa, validating our curation pipeline's 97.6% confidence score. The corpus maintains a 60-40 gender balance across 71,792 utterances. Our work represents a significant leap toward linguistic inclusivity in global AI. The corpus and code are open-sourced, and a demo page is available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17846 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | UrduSpeech: A 156-Hour Urdu Speech Corpus with 12-Dimension Paralinguistic Annotations Haq, Attia Nafees ul Zhu, Zeyu Hu, Jingbin He, ChunJiang Xie, Lei Audio and Speech Processing Despite 230 million speakers, Urdu remains critically under-resourced in speech technology. We introduce UrduSpeech: a large high-fidelity Urdu corpus comprising 156 hours of audio with 12-dimension paralinguistic metadata, encompassing US-Std, US-CS, US-EngPk. To address Right-to-Left script constraints and frequent code-switching, we developed UrduSpeech, a LLM-driven pipeline to curate data across 12 diverse categories, including news, drama, and rare literary forms like Bait-Bazi. We also release a 9-hour US-Benchmark set, manually corrected by native annotators to serve as a standard. Human quality assessment of the primary 156-hour corpus yielded a Mean Opinion Score (MOS) of 4.6 (std = 0.7) with inter-rater reliability confirmed by a 0.68 Cohen's Kappa, validating our curation pipeline's 97.6% confidence score. The corpus maintains a 60-40 gender balance across 71,792 utterances. Our work represents a significant leap toward linguistic inclusivity in global AI. The corpus and code are open-sourced, and a demo page is available. |
| title | UrduSpeech: A 156-Hour Urdu Speech Corpus with 12-Dimension Paralinguistic Annotations |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2605.17846 |