Work

Comparative study on noise-augmented training and its effect on adversarial robustness in ASR systems

Year: 2025

Type: article

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.

Source: Computer Speech & Language

DOI: 10.1016/j.csl.2025.101869

Authors: Karla Pizzi, Matías Pizarro, Asja Fischer

Institutions: Technical University of Munich, Ruhr University Bochum

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,530

Funder: Deutsche Forschungsgemeinschaft