STC Anti-spoofing Systems for the ASVspoof 2015 Challenge
This work addresses spoofing detection for speaker verification systems, but it is incremental as it builds on existing methods like TV-JFA and compares standard classifiers.
The paper tackled the problem of detecting spoofing attacks in automatic speaker verification by exploring acoustic features like phase spectrum and wavelet transforms, resulting in improved efficiency for the STC systems as demonstrated on the ASVspoof 2015 datasets.
This paper presents the Speech Technology Center (STC) systems submitted to Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof) Challenge 2015. In this work we investigate different acoustic feature spaces to determine reliable and robust countermeasures against spoofing attacks. In addition to the commonly used front-end MFCC features we explored features derived from phase spectrum and features based on applying the multiresolution wavelet transform. Similar to state-of-the-art ASV systems, we used the standard TV-JFA approach for probability modelling in spoofing detection systems. Experiments performed on the development and evaluation datasets of the Challenge demonstrate that the use of phase-related and wavelet-based features provides a substantial input into the efficiency of the resulting STC systems. In our research we also focused on the comparison of the linear (SVM) and nonlinear (DBN) classifiers.