Acoustic Simulation Framework for Multi-channel Replay Speech Detection

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. Moreover, previous studies highlighted that generalization of this attack to new environments is challenging, requiring new methods for generating data encompassing various acoustic conditions. Hence, in this work we introduce an acoustic simulation framework designed to simulate multi-channel replay speech configurations using publicly available resources. Using the framework, we train the state-of-the-art multi-channel replay detector M-ALRAD and evaluate its generalisation on the ReMASC real-recording corpus without any real training data. To improve the exploitation of spatial information, we extend M-ALRAD with inter-channel phase difference features computed for adjacent microphone pairs, augmenting the beamformed representation with directional cues. Synthetic datasets are available at https://github.com/michaelneri/synthetic-ReMASC.