Multi-Channel Replay Speech Detection using Acoustic Maps

• URL: http://arxiv.org/abs/2602.16399v1
• Date: Wed, 18 Feb 2026 12:18:45 GMT
• Title: Multi-Channel Replay Speech Detection using Acoustic Maps
• Authors: Michael Neri, Tuomas Virtanen,
• Abstract summary: We propose acoustic maps as a novel spatial feature representation for replay speech detection from multi-channel recordings.<n>A lightweight convolutional neural network is designed to operate on this representation, achieving competitive performance on the ReMASC dataset.
• Score: 8.466109515054315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Replay attacks remain a critical vulnerability for automatic speaker verification systems, particularly in real-time voice assistant applications. In this work, we propose acoustic maps as a novel spatial feature representation for replay speech detection from multi-channel recordings. Derived from classical beamforming over discrete azimuth and elevation grids, acoustic maps encode directional energy distributions that reflect physical differences between human speech radiation and loudspeaker-based replay. A lightweight convolutional neural network is designed to operate on this representation, achieving competitive performance on the ReMASC dataset with approximately 6k trainable parameters. Experimental results show that acoustic maps provide a compact and physically interpretable feature space for replay attack detection across different devices and acoustic environments.

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