Front-End for Anti-Spoofing Countermeasures in Speaker Verification: Scattering Spectral Decomposition

Abstract

As speaker verification is widely used as a means of verifying personal identity in commercial applications, the study of antispoofing countermeasures has become increasingly important. By choosing appropriate spectral and prosodic feature mapping, spoofing methods based on voice conversion and speech synthesis are both capable of deceiving speaker verification systems that typically rely on these features. Consequently alternative front-ends are required for effective spoofing detection. This paper investigates the use of the recently proposed hierarchical scattering decomposition technique, which can be viewed as a generalization of all constant-Q spectral decompositions, to implement front-ends for stand-alone spoofing detection. The coefficients obtained using this decomposition are converted to a feature vector of Scattering Cepstral Coefficients (SCCs). We evaluate the performance of SCCs on the recent spoofing and Antispoofing (SAS) corpus as well as the ASVspoof 2015 challenge corpus and show that SCCs are superior to all other front-ends that have previously been benchmarked on the ASVspoof corpus.