Text-to-Speech (TTS) technology brings significant advantages, such as giving a voice to those with speech impairments, but also enables audio deepfakes and spoofs. The former mislead individuals and may propagate misinformation, while the latter undermine voice biometric security systems. AI-based detection can help to address these challenges by automatically differentiating between genuine and fabricated voice recordings. However, these models are only as good as their training data, which currently is severely limited due to an overwhelming concentration on English and Chinese audio in anti-spoofing databases, thus restricting its worldwide effectiveness. In response, this paper presents the Multi-Language Audio Anti-Spoof Dataset (MLAAD), created using 52 TTS models, comprising 19 different architectures, to generate 160.1 hours of synthetic voice in 23 different languages. We train and evaluate three state-of-the-art deepfake detection models with MLAAD, and observe that MLAAD demonstrates superior performance over comparable datasets like InTheWild or FakeOrReal when used as a training resource. Furthermore, in comparison with 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, both excelling on four datasets. By publishing MLAAD and making trained models accessible via an interactive webserver , we aim to democratize antispoofing technology, making it accessible beyond the realm of specialists, thus contributing to global efforts against audio spoofing and deepfakes.

翻译：文本转语音（TTS）技术带来了显著优势，例如为言语障碍者赋予发声能力，但也催生了音频深度伪造与欺骗技术。前者会误导公众并传播虚假信息，后者则削弱了语音生物特征安全系统。基于人工智能的检测技术可通过自动区分真实与伪造语音记录来应对这些挑战。然而，此类模型的性能高度依赖于训练数据质量——当前防伪数据集严重集中于英语和中文音频，这极大限制了其全球有效性。为此，本文提出多语言音频防伪数据集（MLAAD），该数据集利用52个TTS模型（涵盖19种不同架构），生成了23种语言的160.1小时合成语音。我们使用MLAAD训练并评估了三种先进的深度伪造检测模型，发现MLAAD作为训练资源时，其性能显著优于InTheWild或FakeOrReal等同类数据集。此外，与著名的ASVspoof 2019数据集相比，MLAAD展现出互补价值。在横跨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。通过公开MLAAD数据集并通过交互式网络服务器提供预训练模型，我们致力于推动防伪技术民主化，使其超越专业领域局限，为全球对抗音频欺骗与深度伪造作出贡献。