用语音自然度指导训练，提升语音伪造检测鲁棒性
Naturalness-Aware Curriculum Learning with Dynamic Temperature for Speech Deepfake Detection
- 结合真实标签与主观评分动态评估样本难度，渐进式训练
- 在ASVspoof 2021上实现EER降低23%，无需改模型结构
- 适合关注语音伪造检测泛化能力的研究者和安全应用
近年来，语音伪造检测（SDD）在基于伪影的检测方面取得显著进展，但多数模型忽略了语音自然度这一关键判别线索。本文提出一种自然度感知的课程学习框架，利用语音自然度提升SDD模型的鲁棒性与泛化能力。该方法结合真实标签与平均意见分（MOS）衡量样本难度，动态调整训练顺序，逐步引入更难样本。为进一步增强泛化性，引入基于语音自然度的动态温度缩放机制。在ASVspoof 2021 DF数据集上的实验表明，该方法在不修改模型架构的前提下，实现了相对EER降低23%。消融实验证实了自然度感知训练策略对SDD任务的有效性。
原文摘要 · Abstract (English)
Recent advances in speech deepfake detection (SDD) have significantly improved artifacts-based detection in spoofed speech. However, most models overlook speech naturalness, a crucial cue for distinguishing bona fide speech from spoofed speech. This study proposes naturalness-aware curriculum learning, a novel training framework that leverages speech naturalness to enhance the robustness and generalization of SDD. This approach measures sample difficulty using both ground-truth labels and mean opinion scores, and adjusts the training schedule to progressively introduce more challenging samples. To further improve generalization, a dynamic temperature scaling method based on speech naturalness is incorporated into the training process. A 23% relative reduction in the EER was achieved in the experiments on the ASVspoof 2021 DF dataset, without modifying the model architecture. Ablation studies confirmed the effectiveness of naturalness-aware training strategies for SDD tasks.
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