This paper presents the Multi-Language Audio Anti-Spoofing Dataset (MLAAD), version 10: a dataset of synthetic audio to train and evaluate audio deepfake detection models. It features 175 Text-to-Speech (TTS) models, comprising a total of 1002.9 hours of synthetic voice in 54 different languages. To evaluate this dataset, we train three state-of-the-art deepfake detection models with MLAAD and observe that it demonstrates superior performance to comparable datasets like InTheWild and FakeOrReal when used as a training resource. Moreover, compared to the renowned ASVspoof 2019 dataset, MLAAD proves to be a complementary resource. In tests across eight datasets, MLAAD and ASVspoof 2019 alternately outperformed each other, each excelling on four datasets. By publishing the dataset and making a trained model accessible via an interactive webserver, we aim to democratize anti-spoofing technology, making it accessible beyond the realm of specialists, and contributing to global efforts against audio spoofing and deepfakes.

翻译：本文介绍了多语言音频反欺骗数据集（MLAAD）10.0版本：一个用于训练和评估音频深度伪造检测模型的合成音频数据集。该数据集包含175个文本转语音（TTS）模型，共涵盖54种语言的1002.9小时合成语音。为评估该数据集，我们使用MLAAD训练了三个最先进的深度伪造检测模型，并观察到作为训练资源时，其性能优于InTheWild和FakeOrReal等同类数据集。此外，与著名的ASVspoof 2019数据集相比，MLAAD证明了其互补性。在八个数据集的测试中，MLAAD与ASVspoof 2019交替领先，各自在四个数据集上表现优异。通过发布数据集并通过交互式网络服务器提供训练好的模型，我们旨在推动反欺骗技术的民主化，使其超越专业领域范畴，并为全球应对音频欺骗与深度伪造的努力做出贡献。