🤖 AI Summary
This work addresses the vulnerability of automatic speech recognition (ASR) systems to adversarial perturbations—distortions imperceptible to humans yet capable of inducing transcription errors. The authors propose a neural audio codec based on residual vector quantization (RVQ) that introduces a discrete bottleneck in the signal pathway to suppress adversarial noise while preserving linguistic content. Their analysis reveals a non-monotonic trade-off between quantization depth and robustness, demonstrating that intermediate RVQ depths optimally balance content fidelity and adversarial resilience. Notably, the study establishes, for the first time, a strong correlation between discrete codebook alterations and transcription errors. Experimental results show that the proposed method significantly reduces word error rates across multiple attack types, outperforming conventional compression-based defenses and maintaining robustness even under adaptive attacks.
📝 Abstract
Adversarial perturbations exploit vulnerabilities in automatic speech recognition (ASR) systems while preserving human perceived linguistic content. Neural audio codecs impose a discrete bottleneck that can suppress fine-grained signal variations associated with adversarial noise. We examine how the granularity of this bottleneck, controlled by residual vector quantization (RVQ) depth, shapes adversarial robustness. We observe a non-monotonic trade-off under gradient-based attacks: shallow quantization suppresses adversarial perturbations but degrades speech content, while deeper quantization preserves both content and perturbations. Intermediate depths balance these effects and minimize transcription error. We further show that adversarially induced changes in discrete codebook tokens strongly correlate with transcription error. These gains persist under adaptive attacks, where neural codec configurations outperform traditional compression defenses.