  It is well known that speaker verification systems are subject to spoofing
attacks. The Automatic Speaker Verification Spoofing and Countermeasures
Challenge -- ASVSpoof2015 -- provides a standard spoofing database, containing
attacks based on synthetic speech, along with a protocol for experiments. This
paper describes CPqD's systems submitted to the ASVSpoof2015 Challenge, based
on deep neural networks, working both as a classifier and as a feature
extraction module for a GMM and a SVM classifier. Results show the validity of
this approach, achieving less than 0.5\% EER for known attacks.
