Using Deep Learning for Detecting Spoofing Attacks on Speech Signals
Alan Godoy, Flávio O. Simões, José Augusto Stuchi, Marcus de Assis Angeloni, Mário Uliani, Ricardo P. V. Violato · arXiv (Cornell University) · 2015
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.