In this study, we investigate if noise-augmented training can concurrently improve adversarial robustness in automatic speech recognition (ASR) systems. We conduct a comparative analysis of the adversarial robustness of four different state-of-the-art ASR architectures, where each of the ASR architectures is trained under three different augmentation conditions: one subject to background noise, speed variations, and reverberations, another subject to speed variations only, and a third without any form of data augmentation. The results demonstrate that noise augmentation not only improves model performance on noisy speech but also the model's robustness to adversarial attacks.

翻译：本研究探讨了噪声增强训练是否能够同时提升自动语音识别（ASR）系统的对抗鲁棒性。我们对四种不同的前沿ASR架构进行了对抗鲁棒性的比较分析，其中每种ASR架构均在三种不同的增强条件下进行训练：一种受到背景噪声、语速变化和混响的影响，另一种仅受语速变化影响，第三种则未采用任何形式的数据增强。结果表明，噪声增强不仅能够提升模型在含噪语音上的性能，还能增强模型对对抗攻击的鲁棒性。