Spoofing detection with DNN and one-class SVM for the ASVspoof 2015 challenge

Speaker verification systems have achieved great performance in recent times. However, we usually measure performance on a ideal scenarios with naive impostors that do not modify their voices to impersonate the target speakers. The fact of impersonating a legitimate user is known as spoofing attack. Recent works show the vulnerability of current speaker verification technology to several types of attacks. Most of these works use non-public databases and different performance measures, which makes difficult to compare approaches. The spoofing challenge (ASVspoof 2015) tries to overcome this problem by proposing a common evaluation framework. This paper describes our submission to the challenge. We proposed to use spectral log-filter-bank and relative phase shift features as input to classifiers based on deep neural networks (DNN). The first of our classifiers used DNN posteriors to decide if the trial is spoof or non-spoof. The second used a bottleneck feature from the DNN as input to a one-class SVM. The one-class SVM models the distribution of legitimate speech, not needing spoofing data for training. We fused the score of the different classifiers to produce our final submission. Our system attained very competitive results with EER<0.05% in 9 out of 10 spoofing types.