Title: Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes,
Abstract: In this paper, we propose a replay attack spoofing detection system for
automatic speaker verification using multitask learning of noise classes. We
define the noise that is caused by the replay attack as replay noise. We
explore the effectiveness of training a deep neural network simultaneously for
replay attack spoofing detection and replay noise classification. The
multi-task learning includes classifying the noise of playback devices,
recording environments, and recording devices as well as the spoofing
detection. Each of the three types of the noise classes also includes a genuine
class. The experiment results on the ASVspoof2017 datasets demonstrate that the
performance of our proposed system is improved by 30% relatively on the
evaluation set.