STC Anti-spoofing Systems for the ASVspoof 2015 Challenge

TL;DR

This paper evaluates various acoustic features and classifiers in developing robust anti-spoofing systems for speaker verification, demonstrating that phase and wavelet features enhance detection performance.

Contribution

It introduces the use of phase spectrum and wavelet-based features in anti-spoofing systems and compares linear and nonlinear classifiers for improved detection.

Findings

Phase and wavelet features improve anti-spoofing accuracy.

Nonlinear classifiers outperform linear ones in detection tasks.

Features significantly enhance system robustness against spoofing.

Abstract

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.