  Speaker recognition technology is applied in various tasks ranging from
personal virtual assistants to secure access systems. However, the robustness
of these systems against adversarial attacks, particularly to additive
perturbations, remains a significant challenge. In this paper, we pioneer
applying robustness certification techniques to speaker recognition, originally
developed for the image domain. In our work, we cover this gap by transferring
and improving randomized smoothing certification techniques against
norm-bounded additive perturbations for classification and few-shot learning
tasks to speaker recognition. We demonstrate the effectiveness of these methods
on VoxCeleb 1 and 2 datasets for several models. We expect this work to improve
voice-biometry robustness, establish a new certification benchmark, and
accelerate research of certification methods in the audio domain.
