Abstract
Spoofing detection systems are typically trained using diverse recordings from multiple speakers, often assuming that the resulting embeddings are independent of speaker identity. However, this assumption remains unverified. In this paper, we investigate the impact of speaker information on spoofing detection systems. We propose two approaches within our Speaker-Invariant Multi-Task framework, one that models speaker identity within the embeddings and another that removes it. SInMT integrates multi-task learning for joint speaker recognition and spoofing detection, incorporating a gradient reversal layer. Evaluated using four datasets, our speaker-invariant model reduces the average equal error rate by 17% compared to the baseline, with up to 48% reduction for the most challenging attacks (e.g., A11).
Abstract (translated)
声纹伪造检测系统通常使用来自多位说话者的多样化录音进行训练，常常假设由此产生的嵌入（embeddings）与说话人身份无关。然而，这一假设尚未得到验证。本文研究了说话人信息对伪造检测系统的影响。我们提出了两种方法，在我们的说话人不变的多任务框架（Speaker-Invariant Multi-Task, SInMT）中实现：一种是将说话人身份建模到嵌入中，另一种则是去除该身份信息。SInMT整合了用于联合进行说话人识别和伪造检测的多任务学习，并加入了一个梯度反转层。通过四个数据集的评估，我们的说话人不变模型与基准相比平均等错误率降低了17%，在最具有挑战性的攻击（如A11）情况下，甚至可以降低高达48%的等错误率。
URL
https://arxiv.org/abs/2602.20805
