用Mamba模型提升语音欺骗检测效率与准确率
XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection
- 双列双向Mamba结构结合自监督学习
- 在ASVspoof 2021和In-the-Wild数据集上表现领先
- 适合语音安全领域研究者和系统开发者
Transformer及其变体在语音处理中取得显著进展，但其多头自注意力机制计算成本高。为此，新型选择性状态空间模型Mamba被提出作为替代方案。基于其在自动语音识别中的成功，本文将Mamba应用于语音欺骗攻击检测。由于能有效处理长序列，Mamba可捕捉伪造语音中的异常特征。然而，在标注数据有限时性能下降。为此，我们提出一种基于双列架构的Mamba新结构，并结合预训练wav2vec 2.0模型进行自监督学习。实验表明，该方法在ASVspoof 2021 LA和DF数据集上达到竞争性效果且推理更快；在更具挑战性的In-the-Wild数据集上，成为最强检测方案。代码已公开于https://github.com/swagshaw/XLSR-Mamba。
原文摘要 · Abstract (English)
Transformers and their variants have achieved great success in speech processing. However, their multi-head self-attention mechanism is computationally expensive. Therefore, one novel selective state space model, Mamba, has been proposed as an alternative. Building on its success in automatic speech recognition, we apply Mamba for spoofing attack detection. Mamba is well-suited for this task as it can capture the artifacts in spoofed speech signals by handling long-length sequences. However, Mamba's performance may suffer when it is trained with limited labeled data. To mitigate this, we propose combining a new structure of Mamba based on a dual-column architecture with self-supervised learning, using the pre-trained wav2vec 2.0 model. The experiments show that our proposed approach achieves competitive results and faster inference on the ASVspoof 2021 LA and DF datasets, and on the more challenging In-the-Wild dataset, it emerges as the strongest candidate for spoofing attack detection. The code has been publicly released in https://github.com/swagshaw/XLSR-Mamba.
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