6 accepted papers
Automatic Speech Recognition (ASR) systems must be robust to the myriad types of noises present in real-world environments including environmental noise, room impulse response, special effects as well as attacks by malicious actors (adversarial attacks). Recent works seek to improve accuracy and rob…
As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and digital world. We propose Speech Robust Bench (SRB), a comprehensive benchmark for evaluating the robustness of ASR mo…
Vulnerability to adversarial perturbations is a major flaw of Deep Neural Networks (DNNs) that brings their reliability in real-world scenarios into question. However, biological perception, which DNNs emulate, is highly robust to such perturbations. This suggests that DNNs diverge from biological p…
Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks -- subtle, perceptually indistinguishable perturbations of inputs that change the response of the model. In the context of vision, we hypothesize that an important contributor to the robustness of human visual perce…
Recent work suggests that adversarial examples are enabled by high-frequency components in the dataset. In the speech domain where spectrograms are used extensively, masking those components seems like a sound direction for defenses against attacks. We explore a smoothing approach based on additive…
Deep Neural Networks (DNNs), while providing state-of-the-art performance in a wide variety of tasks, have been shown to be vulnerable to adversarial attacks. Recent studies have posited that this vulnerability arises because DNNs operate over a grossly overspecified input space with very sparse hum…