标题： 后训练用于深度伪造语音检测 显示英文标题

标题： Post-training for Deepfake Speech Detection

摘要： 我们引入了一种后训练方法，通过弥合通用预训练和领域特定微调之间的差距，将自监督学习（SSL）模型适应于深度伪造语音检测。 我们提出了AntiDeepfake模型，这是一系列使用包含超过56,000小时真实语音和超过一百种语言中18,000小时具有各种伪影的语音的大规模多语言语音数据集开发的后训练模型。 实验结果表明，这些后训练模型已经表现出对未见过的深度伪造语音的强大鲁棒性和泛化能力。 当它们在Deepfake-Eval-2024数据集上进一步微调时，这些模型始终超越不利用后训练的现有最先进检测器。 模型检查点和源代码可在网上获取。 显示英文摘要

摘要： We introduce a post-training approach that adapts self-supervised learning (SSL) models for deepfake speech detection by bridging the gap between general pre-training and domain-specific fine-tuning. We present AntiDeepfake models, a series of post-trained models developed using a large-scale multilingual speech dataset containing over 56,000 hours of genuine speech and 18,000 hours of speech with various artifacts in over one hundred languages. Experimental results show that the post-trained models already exhibit strong robustness and generalization to unseen deepfake speech. When they are further fine-tuned on the Deepfake-Eval-2024 dataset, these models consistently surpass existing state-of-the-art detectors that do not leverage post-training. Model checkpoints and source code are available online.