  Voice-based biometric systems are highly prone to spoofing attacks. Recently,
various countermeasures have been developed for detecting different kinds of
attacks such as replay, speech synthesis (SS) and voice conversion (VC). Most
of the existing studies are conducted with a specific training set defined by
the evaluation protocol. However, for realistic scenarios, selecting
appropriate training data is an open challenge for the system administrator.
Motivated by this practical concern, this work investigates the generalization
capability of spoofing countermeasures in restricted training conditions where
speech from a broad attack types are left out in the training database. We
demonstrate that different spoofing types have considerably different
generalization capabilities. For this study, we analyze the performance using
two kinds of features, mel-frequency cepstral coefficients (MFCCs) which are
considered as baseline and recently proposed constant Q cepstral coefficients
(CQCCs). The experiments are conducted with standard Gaussian mixture model -
maximum likelihood (GMM-ML) classifier on two recently released spoofing
corpora: ASVspoof 2015 and BTAS 2016 that includes cross-corpora performance
analysis. Feature-level analysis suggests that static and dynamic coefficients
of spectral features, both are important for detecting spoofing attacks in the
real-life condition.
