---
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.