Reassessing Noise Augmentation Methods in the Context of Adversarial Speech
<|reference_start|>Reassessing Noise Augmentation Methods in the Context of Adversarial Speech: 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.<|reference_end|>
@article{pizzi2024reassessing,
    title={Reassessing Noise Augmentation Methods in the Context of Adversarial
  Speech},
    author={Karla Pizzi, Mat'ias Pizarro, Asja Fischer},
    journal={Proc. 4th Symposium on Security and Privacy in Speech
  Communication, 26-32, 2024},
    year={2024},
    doi={10.21437/SPSC.2024-5},
    archivePrefix={arXiv},
    eprint={2409.01813},
    primaryClass={eess.AS cs.LG cs.SD}
}