你还记得吗？克服虚假音频检测中的灾难性遗忘
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection
AI总结：
针对虚假音频检测中的灾难性遗忘问题，提出正则化自适应权重修改（RAWM）持续学习算法，依据真假音频比例自适应调整权重修改方向并引入正则化约束，在跨数据集实验中显著提升性能。
摘要：
当前的虚假音频检测算法在大多数数据集上已取得有前景的性能。然而，当处理不同数据集的音频时，其性能可能会显著下降。用于克服灾难性遗忘的正交权重修改方法没有考虑不同数据集之间真实音频的相似性。为克服这一局限，我们提出了一种用于虚假音频检测的持续学习算法，以克服灾难性遗忘，称为正则化自适应权重修改（RAWM）。在微调检测网络时，我们的方法根据真实话语与虚假话语的比例自适应地计算权重修改方向。自适应修改方向确保网络能够在新数据集上有效检测虚假音频，同时保留其对旧模型的知识，从而缓解灾难性遗忘。此外，从差异很大的声学条件中采集的真实音频可能会使其特征分布发生偏移，因此我们引入一个正则化约束，以迫使网络在这方面记住旧分布。我们的方法可以很容易地推广到相关领域，如语音情感识别。我们还在多个数据集上评估了我们的方法，并在跨数据集实验中获得了显著的性能提升。
英文摘要：
Current fake audio detection algorithms have achieved promising performances on most datasets. However, their performance may be significantly degraded when dealing with audio of a different dataset. The orthogonal weight modification to overcome catastrophic forgetting does not consider the similarity of genuine audio across different datasets. To overcome this limitation, we propose a continual learning algorithm for fake audio detection to overcome catastrophic forgetting, called Regularized Adaptive Weight Modification (RAWM). When fine-tuning a detection network, our approach adaptively computes the direction of weight modification according to the ratio of genuine utterances and fake utterances. The adaptive modification direction ensures the network can effectively detect fake audio on the new dataset while preserving its knowledge of old model, thus mitigating catastrophic forgetting. In addition, genuine audio collected from quite different acoustic conditions may skew their feature distribution, so we introduce a regularization constraint to force the network to remember the old distribution in this regard. Our method can easily be generalized to related fields, like speech emotion recognition. We also evaluate our approach across multiple datasets and obtain a significant performance improvement on cross-dataset experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
