The SJTU Robust Anti-Spoofing System for the ASVspoof 2019 Challenge

The robustness of an anti-spoofing system is progressively more important in order to develop a reliable speaker verification system. Previous challenges and datasets mainly focus on a specific type of spoofing attacks. The ASVspoof 2019 edition is the first challenge to address two major spoofing types - logical and physical access. This paper presents the SJTU's submitted anti-spoofing system to the ASVspoof 2019 challenge. Log-CQT features are developed in conjunction with multi-layer convolutional neural networks for robust performance across both subtasks. CNNs with gradient linear units (GLU) activations are utilized for spoofing detection. The proposed system shows consistent performance improvement over all types of spoofing attacks. Our primary submissions achieve the 5(th) and 8(th) positions for the logical and physical access respectively. Moreover, our contrastive submission to the PA task exhibits better generalization compared to our primary submission, and achieves a comparable performance to the 3(rd) position of the challenge.