Spoof detection using x-vector and feature switching

Detecting spoofed utterances is a fundamental problem in voice-based biometrics. Spoofing can be performed either by logical accesses like speech synthesis, voice conversion or by physical accesses such as replaying the pre-recorded utterance. Inspired by the state-of-the-art x-vector based speaker verification approach, this paper proposes a deep neural network (DNN) architecture for spoof detection from both logical and physical access. A novelty of the x-vector approach vis-a-vis conventional DNN based systems is that it can handle variable length utterances during testing. Performance of the proposed x-vector systems and the baseline Gaussian mixture model (GMM) systems is analyzed on the ASV-spoof-2019 dataset. The proposed system surpasses the GMM system for physical access, whereas the GMM system detects logical access better. Compared to the GMM systems, the proposed x-vector approach gives an average relative improvement of 14.64% for physical access. When combined with the decision-level feature switching (DLFS) paradigm, the best system in the proposed approach outperforms the best baseline systems with a relative improvement of 67.48% and 40.04% for both logical and physical access in terms of minimum tandem cost detection function (min-t-DCF), respectively.