Influence of the attack conditions on countermeasures for Automatic Speaker Verification

The ASVSpoof challenges goal is to evaluate countermeasures to spoof attacks on automatic speaker verification systems. We first analyze in more details the results of the baseline systems provided by the organization and unveil several weaknesses for some types of attack. In particular for the physical access (PA) task, replay attacks with low reverberation time and/or high quality of the replay device are problematic. Based on this observation, we propose several improvements. Firstly, a specific learning targeting the problematic types of attack. Secondly, a new type of feature enhancing the reverberation. Thirdly, a Deep Neural Network with more modelling capability. On the development set of the PA task, each proposed improvements show results ameliora-tion for the targeted types of attack. Furthermore, the ensemble systems based on this proposed improvements show great overall results amelioration compared to the baseline (0.140 vs 0.193 min t-DCF). However, the amelioration is less encouraging on the evaluation set (0.225 vs 0.245 min t-DCF), thus raising the question of over-fitting as the development set and the train set are similar