Domain-Incremental Learning for Multi-Channel Replay Speech Detection
First continual learning benchmark for multi-channel replay speech detection shows task-specific beamforming cuts catastrophic forgetting across 24 acoustic environments.
Researchers frame replay-attack detection for voice-controlled systems as domain-incremental learning over acoustic environments, evaluating a beamformer-based detector across all 24 environment orderings of the ReMASC corpus with five seeds. Naive sequential fine-tuning raises error rates on previously learned environments by 18.8 points, while elastic weight consolidation halves forgetting but loses plasticity and gradient projection memory is statistically indistinguishable from naive fine-tuning. A task-specific beamformer keeping one spatial front-end per environment significantly improves final and incremental accuracy, and the last environment in a sequence dominates final performance.
- Sequential fine-tuning forgets severely, +18.8 error-rate points on old environments
- EWC halves forgetting but loses plasticity; GPM matches naive fine-tuning
- Task-specific beamformer (TSB) improves final and incremental accuracy
- Last environment in the sequence dominates final performance
Full article172 words · extracted from arxiv.org · click to collapse
Replay attacks are the most accessible threat to voice-controlled systems, and the acoustic cues that expose them are strongly modulated by the environment in which the attack is mounted. A detector deployed in the field therefore has to absorb new acoustic conditions over time, ideally without revisiting past recordings, since retaining speech indefinitely is both expensive and legally constrained. We frame this as Domain-Incremental Learning (DIL) over acoustic environments and present the first continual learning benchmark for multi-channel replay speech detection, evaluating a state-of-the-art beamformer-based detector over all 24 environment orderings of the ReMASC corpus with five seeds. Sequential fine-tuning forgets severely, raising the error rate on previously learned environments by 18.8 points. Elastic weight consolidation (EWC) halves forgetting but loses plasticity, gradient projection memory (GPM) is statistically indistinguishable from naive fine-tuning, and the proposed task-specific beamformer (TSB) that keeps one spatial front-end per environment significantly improves final and incremental accuracy. We further show that the last environment of the sequence dominates final performance. Code, results, and analysis are available at https://github.com/michaelneri/replay-speech-continual.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.11194