用自适应波束成形检测语音重放攻击，提升复杂环境下的识别准确率。
Multi-channel Replay Speech Detection using an Adaptive Learnable Beamformer
- 融合可学习波束成形与卷积循环网络，联合优化空间滤波与分类。
- 在多麦克风ReMASC数据集上超越现有方法，尤其在复杂环境中提升显著。
- 对未见过的环境有更强泛化能力，适合实际部署场景使用。
重放攻击是语音控制系统面临的严重威胁，攻击者通过播放录音语音获取非法访问权限。本文提出一种基于多通道音频特征的神经网络架构M-ALRAD，用于检测重放攻击。该方法结合可学习自适应波束成形与卷积循环神经网络，实现空间滤波与分类的联合优化。在包含四麦克风阵列配置及四种环境的ReMASC数据集上进行实验，结果表明该方法优于现有最先进水平，尤其在挑战性声学环境下表现突出。此外，相比先前研究，本方法在未见环境中的泛化能力更强。
原文摘要 · Abstract (English)
Replay attacks belong to the class of severe threats against voice-controlled systems, exploiting the easy accessibility of speech signals by recorded and replayed speech to grant unauthorized access to sensitive data. In this work, we propose a multi-channel neural network architecture called M-ALRAD for the detection of replay attacks based on spatial audio features. This approach integrates a learnable adaptive beamformer with a convolutional recurrent neural network, allowing for joint optimization of spatial filtering and classification. Experiments have been carried out on the ReMASC dataset, which is a state-of-the-art multi-channel replay speech detection dataset encompassing four microphones with diverse array configurations and four environments. Results on the ReMASC dataset show the superiority of the approach compared to the state-of-the-art and yield substantial improvements for challenging acoustic environments. In addition, we demonstrate that our approach is able to better generalize to unseen environments with respect to prior studies.
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