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Recently, the vulnerability of automatic speaker recognition systems to spoofing attacks has received significant interest among researchers. A robust speaker recognition system demands not only high recognition accuracy but also robustness to spoofing attacks. Several spoofing and countermeasure challenges have been organized to draw attention to this problem among the speaker recognition communities. Low-level descriptors designed to detect artifacts in spoofed speech are found to be the most effective countermeasures against spoofing attacks. In this work, we used Fisher vector encoding of low-level descriptors extracted from speech signals. The idea behind Fisher vector encoding is to determine the amount of change induced by the descriptors of the signal on a background probability model which is typically a Gaussian mixture model. The Fisher vector encodes the amount of change of the model parameters to optimally fit the new- coming data. For performance evaluation of the proposed approach we carried out spoofing detection experiments on the 2015 edition of automatic speaker verification spoofing and countermeasure challenge (ASVspoof2015) and report results on the evaluation set. As baseline systems, we used the standard Gaussian mixture model and i-vector/PLDA paradigms. For a fair comparison, in all systems, Constant Q cepstral coefficient (CQCC) features were used as low-level descriptors. With the Fisher vector-based approach, we achieved an equal error rate (EER) of 0.1145% on the known attacks, 1.223% on the unknown attacks, and 0.668% on the average. Moreover, with a single decision threshold this approach yielded an EER of 1.05% on the evaluation set.

On the Use of Fisher Vector Encoding for Voice Spoofing Detection