  Automatic Speaker Verification (ASV) is the process of identifying a person
based on the voice presented to a system. Different synthetic approaches allow
spoofing to deceive ASV systems (ASVs), whether using techniques to imitate a
voice or recunstruct the features. Attackers try to beat up the ASVs using four
general techniques; impersonation, speech synthesis, voice conversion, and
replay. The last technique is considered as a common and high potential tool
for spoofing purposes since replay attacks are more accessible and require no
technical knowledge from adversaries. In this study, we introduce a novel
replay spoofing countermeasure for ASVs. Accordingly, we used the Constant Q
Cepstral Coefficient (CQCC) features fed into an autoencoder to attain more
informative features and to consider the noise information of spoofed
utterances for discrimination purpose. Finally, different configurations of the
Siamese network were used for the first time in this context for
classification. The experiments performed on ASVspoof challenge 2019 dataset
using Equal Error Rate (EER) and Tandem Detection Cost Function (t-DCF) as
evaluation metrics show that the proposed system improved the results over the
baseline by 10.73% and 0.2344 in terms of EER and t-DCF, respectively.
