  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.
