Datasets:
| language: | |
| - ab | |
| - hy | |
| - as | |
| - av | |
| - az | |
| - ba | |
| - be | |
| - pt | |
| - bg | |
| - ca | |
| - ce | |
| - cv | |
| - hr | |
| - cs | |
| - da | |
| - nl | |
| - en | |
| - fa | |
| - fi | |
| - fr | |
| - ka | |
| - gu | |
| - hi | |
| - hu | |
| - is | |
| - id | |
| - it | |
| - ja | |
| - kk | |
| - ko | |
| - ky | |
| - lv | |
| - lt | |
| - lb | |
| - ms | |
| - ml | |
| - mr | |
| - ne | |
| - or | |
| - os | |
| - pl | |
| - ru | |
| - sl | |
| - es | |
| - sw | |
| - te | |
| - tk | |
| - uz | |
| - cy | |
| - aus | |
| - brx | |
| - arr | |
| - crh | |
| - myv | |
| - xal | |
| - krc | |
| - kjh | |
| - lez | |
| - mni | |
| - mhr | |
| - mdf | |
| - nog | |
| - raj | |
| - tt | |
| - tyv | |
| - sah | |
| license: mit | |
| size_categories: | |
| - 1M<n<10M | |
| task_categories: | |
| - audio-classification | |
| pretty_name: LRL spoof | |
| tags: | |
| - antispoofing | |
| # LRLspoof: Low-Resource Language Spoofing Corpus | |
| LRLspoof is a large-scale multilingual synthetic-speech corpus designed for cross-lingual spoof detection. | |
| The dataset was introduced in the paper [When Spoof Detectors Travel: Evaluation Across 66 Languages in the Low-Resource Language Spoofing Corpus](https://huggingface.co/papers/2603.02364). | |
| ### Dataset Summary | |
| - **Total Audio:** 2,732 hours | |
| - **Number of Languages:** 66 (including 45 low-resource languages) | |
| - **Generation:** Audio was generated using 24 different open-source Text-to-Speech (TTS) systems. | |
| - **Purpose:** Benchmarking countermeasures for synthetic speech detection (spoofing) across diverse linguistic domains. | |
| ### Key Features | |
| * **Multilingual Scope:** Covers a wide variety of languages to evaluate how language-specific features act as a source of domain shift in spoof detection. | |
| * **Scale:** One of the largest available corpora for synthetic speech detection, facilitating robust training and evaluation. | |
| * **Low-Resource Focus:** Explicitly includes 45 languages defined as low-resource, addressing a gap in current AI safety and security research. |