平衡真实语音与生成模型数据，提升深伪语音检测泛化能力
A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators
- 通过控制真实语音和生成模型数据的配比，研究其对检测性能的影响
- 实验表明，均衡的数据分布使模型在跨数据集测试中表现更优
- 适合需要高泛化性的深伪语音检测系统开发者参考
本文分析了影响深伪语音检测（DSD）模型性能与泛化能力的两个关键因素：真实语音资源（BR）和AI生成器（AG）。首先提出一个基准深度学习模型，并通过实验揭示了BR与AG对推理阶段阈值判定的影响。基于实验结果，构建了一个新数据集，该数据集整合了公开的DSD数据，并在BR与AG之间保持平衡。在此数据集上训练多个深度学习模型，并在多个基准数据集上进行跨数据集评估。结果表明，维持BR与AG的平衡是训练通用性DSD模型的关键。
原文摘要 · Abstract (English)
In this paper, we analyze two main factors of Bonafide Resource (BR) or AI-based Generator (AG) which affect the performance and the generality of a Deepfake Speech Detection (DSD) model. To this end, we first propose a deep-learning based model, referred to as the baseline. Then, we conducted experiments on the baseline by which we indicate how Bonafide Resource (BR) and AI-based Generator (AG) factors affect the threshold score used to detect fake or bonafide input audio in the inference process. Given the experimental results, a dataset, which re-uses public Deepfake Speech Detection (DSD) datasets and shows a balance between Bonafide Resource (BR) or AI-based Generator (AG), is proposed. We then train various deep-learning based models on the proposed dataset and conduct cross-dataset evaluation on different benchmark datasets. The cross-dataset evaluation results prove that the balance of Bonafide Resources (BR) and AI-based Generators (AG) is the key factor to train and achieve a general Deepfake Speech Detection (DSD) model.
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