  The state-of-art models for speech synthesis and voice conversion are capable
of generating synthetic speech that is perceptually indistinguishable from
bonafide human speech. These methods represent a threat to the automatic
speaker verification (ASV) systems. Additionally, replay attacks where the
attacker uses a speaker to replay a previously recorded genuine human speech
are also possible. We present our solution for the ASVSpoof2019 competition,
which aims to develop countermeasure systems that distinguish between spoofing
attacks and genuine speeches. Our model is inspired by the success of residual
convolutional networks in many classification tasks. We build three variants of
a residual convolutional neural network that accept different feature
representations (MFCC, Log-magnitude STFT, and CQCC) of input. We compare the
performance achieved by our model variants and the competition baseline models.
In the logical access scenario, the fusion of our models has zero t-DCF cost
and zero equal error rate (EER), as evaluated on the development set. On the
evaluation set, our model fusion improves the t-DCF and EER by 25% compared to
the baseline algorithms. Against physical access replay attacks, our model
fusion improves the baseline algorithms t-DCF and EER scores by 71% and 75% on
the evaluation set, respectively.
