arXiv:2602.16399v2 Announce Type: replace-cross 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
The proliferation of voice assistant applications and the increasing sophistication of AI models make robust speaker verification systems a critical current challenge.
This development enhances the security and reliability of AI-powered voice interfaces, directly impacting their trustworthiness and adoption in sensitive applications.
Speaker verification systems can now better defend against sophisticated replay attacks by leveraging spatial acoustic features from multi-channel recordings.
- · Voice assistant developers
- · Security-conscious industries
- · Consumers of voice AI
- · Attackers attempting replay fraud
Improved security for voice authentication in banking, smart home, and automotive sectors.
Increased user confidence in voice AI, potentially accelerating its integration into more critical infrastructure.
The development of more complex and multi-modal anti-spoofing countermeasures as attackers adapt.
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