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

• URL: http://arxiv.org/abs/2409.01813v4
• Date: Fri, 07 Nov 2025 17:07:19 GMT
• Title: Comparative Study on Noise-Augmented Training and its Effect on Adversarial Robustness in ASR Systems
• Authors: Karla Pizzi, Matías Pizarro, Asja Fischer,
• Abstract summary: We conduct a comparative analysis of the adversarial robustness of four different ASR architectures.<n>We then evaluate the robustness of all resulting models against attacks with white-box or black-box adversarial examples.<n>Our results demonstrate that noise augmentation not only enhances model performance on noisy speech but also improves the model's robustness to adversarial attacks.
• Score: 11.345811825870504
• License: http://creativecommons.org/licenses/by/4.0/
• 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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