标题： 基于多头注意力的调制谱图与SSL融合用于虚假语音检测 显示英文标题

标题： Fusion of Modulation Spectrogram and SSL with Multi-head Attention for Fake Speech Detection

摘要： 伪造语音检测系统已成为对抗语音深度伪造的必要手段。 当前系统由于缺乏多样化的训练数据，在域外语音样本上表现出较差的泛化能力。 在本文中，我们尝试通过提出一种使用自监督（SSL）语音嵌入和调制谱图（MS）特征的新语音表示来解决域泛化问题。 采用融合策略结合两种语音表示，以引入分类任务的新前端。 提出的SSL+MS融合表示传递给AASIST后端网络。 在单语种和多语种伪造语音数据集上进行实验，以评估所提模型架构在跨数据集和多语种情况下的有效性。 与基线相比，所提出的模型在域内设置下分别在ASVspoof 2019和MLAAD数据集上实现了37%和20%的相对性能提升。 在域外场景中，基于ASVspoof 2019训练的模型在MLAAD数据集上评估时显示出36%的相对改进。 在所有评估的语言中，所提出的模型始终优于基线，表明域泛化能力得到了增强。 显示英文摘要

摘要： Fake speech detection systems have become a necessity to combat against speech deepfakes. Current systems exhibit poor generalizability on out-of-domain speech samples due to lack to diverse training data. In this paper, we attempt to address domain generalization issue by proposing a novel speech representation using self-supervised (SSL) speech embeddings and the Modulation Spectrogram (MS) feature. A fusion strategy is used to combine both speech representations to introduce a new front-end for the classification task. The proposed SSL+MS fusion representation is passed to the AASIST back-end network. Experiments are conducted on monolingual and multilingual fake speech datasets to evaluate the efficacy of the proposed model architecture in cross-dataset and multilingual cases. The proposed model achieves a relative performance improvement of 37% and 20% on the ASVspoof 2019 and MLAAD datasets, respectively, in in-domain settings compared to the baseline. In the out-of-domain scenario, the model trained on ASVspoof 2019 shows a 36% relative improvement when evaluated on the MLAAD dataset. Across all evaluated languages, the proposed model consistently outperforms the baseline, indicating enhanced domain generalization.