Title: Acoustic Simulation Framework for Multi-channel Replay Speech Detection Show Chinese title

Title: 多通道回放语音检测的声学仿真框架

Abstract: Replay speech attacks pose a significant threat to voice-controlled systems, especially in smart environments where voice assistants are widely deployed. While multi-channel audio offers spatial cues that can enhance replay detection robustness, existing datasets and methods predominantly rely on single-channel recordings. In this work, we introduce an acoustic simulation framework designed to simulate multi-channel replay speech configurations using publicly available resources. Our setup models both genuine and spoofed speech across varied environments, including realistic microphone and loudspeaker impulse responses, room acoustics, and noise conditions. The framework employs measured loudspeaker directionalities during the replay attack to improve the realism of the simulation. We define two spoofing settings, which simulate whether a reverberant or an anechoic speech is used in the replay scenario, and evaluate the impact of omnidirectional and diffuse noise on detection performance. Using the state-of-the-art M-ALRAD model for replay speech detection, we demonstrate that synthetic data can support the generalization capabilities of the detector across unseen enclosures. Show Chinese abstract

Abstract: 回放语音攻击对语音控制的系统构成重大威胁，尤其是在语音助手广泛部署的智能环境中。 虽然多通道音频提供了空间线索，可以增强回放检测的鲁棒性，但现有的数据集和方法主要依赖于单通道录音。 在本工作中，我们引入了一个声学模拟框架，旨在使用公开可用的资源模拟多通道回放语音配置。 我们的设置在各种环境中对真实和伪造语音进行建模，包括现实的麦克风和扬声器脉冲响应、房间声学和噪声条件。 该框架在回放攻击期间使用测量的扬声器方向性以提高模拟的真实性。 我们定义了两种伪造设置，模拟在回放场景中是否使用混响或无回声语音，并评估全向和扩散噪声对检测性能的影响。 使用最先进的 M-ALRAD 模型进行回放语音检测，我们证明合成数据可以支持检测器在未见过的封闭环境中的泛化能力。