# When Spoof Detectors Travel: Evaluation Across 66 Languages in the Low-Resource Language Spoofing Corpus

arXiv: https://arxiv.org/abs/2603.02364 · HTML: https://mlsanity.com/paper/2603.02364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection](https://mlsanity.com/paper/2609.16458/) (2026)

## Abstract (arXiv)

We introduce LRLspoof, a large-scale multilingual synthetic-speech corpus for cross-lingual spoof detection, comprising 2,732 hours of audio generated with 24 open-source TTS systems across 66 languages, including 45 low-resource languages under our operational definition. To evaluate robustness without requiring target-domain bonafide speech, we benchmark 11 publicly available countermeasures using threshold transfer: for each model we calibrate an EER operating point on pooled external benchmarks and apply the resulting threshold, reporting spoof rejection rate (SRR). Results show model-dependent cross-lingual disparity, with spoof rejection varying markedly across languages even under controlled conditions, highlighting language as an independent source of domain shift in spoof detection. The dataset is publicly available at hrefhttps://huggingface.co/datasets/lab260/LRLspooftextbfunderlinetextitHuggingFace and hrefhttps://modelscope.cn/datasets/lab260/LRLspooftextbfunderlinetextitModelScope