解决语音伪造检测中数据增强导致的梯度冲突问题
Addressing Gradient Misalignment in Data-Augmented Training for Robust Speech Deepfake Detection
- 设计双路径框架，对原始与增强语音的梯度方向进行对齐
- 在In-the-Wild数据集上降低18.69%等错误率，加速收敛
- 适合需要提升鲁棒性的语音伪造检测研究者
在语音深度伪造检测（SDD）中，数据增强（DA）常用于提升模型在不同语音条件和欺骗攻击下的泛化能力。然而，训练过程中原始输入与增强输入的反向传播梯度可能发生错位，导致参数更新冲突。这种冲突会阻碍收敛，使模型陷入次优解，从而削弱数据增强的收益。为探究并解决该问题，本文提出一种带有梯度对齐的双路径数据增强（DPDA）训练框架。每个训练语句通过两条路径处理：一条使用原始语音，另一条使用其增强版本。该设计允许比较并对齐两者的反向梯度方向，以减少优化冲突。分析显示，使用RawBoost增强时，约25%的训练迭代存在原始输入与增强版本间的梯度冲突。通过梯度对齐修复冲突，本方法可减少训练轮数，并在In-the-Wild数据集上实现高达18.69%的等错误率相对下降。
原文摘要 · Abstract (English)
In speech deepfake detection (SDD), data augmentation (DA) is commonly used to improve model generalization across varied speech conditions and spoofing attacks. However, during training, the backpropagated gradients from original and augmented inputs may misalign, which can result in conflicting parameter updates. These conflicts could hinder convergence and push the model toward suboptimal solutions, thereby reducing the benefits of DA. To investigate and address this issue, we design a dual-path data-augmented (DPDA) training framework with gradient alignment for SDD. In our framework, each training utterance is processed through two input paths: one using the original speech and the other with its augmented version. This design allows us to compare and align their backpropagated gradient directions to reduce optimization conflicts. Our analysis shows that approximately 25% of training iterations exhibit gradient conflicts between the original inputs and their augmented counterparts when using RawBoost augmentation. By resolving these conflicts with gradient alignment, our method accelerates convergence by reducing the number of training epochs and achieves up to an 18.69% relative reduction in Equal Error Rate on the In-the-Wild dataset compared to the baseline.
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