Comparative Study on Noise-Augmented Training and its Effect on Adversarial Robustness in ASR Systems

Karla Pizzi; Matías Pizarro; Asja Fischer

doi:10.1016/j.csl.2025.101869

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arXiv · 2409.01813

Comparative Study on Noise-Augmented Training and its Effect on Adversarial Robustness in ASR Systems

Karla Pizzi·Matías Pizarro·Asja Fischer

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Abstract

In this study, we investigate whether 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 ASR architectures, each trained under three different augmentation conditions: (1) background noise, speed variations, and reverberations; (2) speed variations only; (3) no data augmentation. We then evaluate the robustness of all resulting models against attacks with white-box or black-box adversarial examples. Our results demonstrate that noise augmentation not only enhances model performance on noisy speech but also improves the model's robustness to adversarial attacks.

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Karla Pizzi, Matías Pizarro, Asja Fischer. 2024-09-03. Comparative Study on Noise-Augmented Training and its Effect on Adversarial Robustness in ASR Systems. https://doi.org/10.1016/j.csl.2025.101869

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