Performance evaluation of front- and back-end techniques for ASV spoofing detection systems based on deep features

As Automatic Speaker Verification (ASV) becomes more popular, so do the ways impostors can use to gain illegal access to speech-based biometric systems. For instance, impostors can use Text-to-Speech (TTS) and Voice Conversion (VC) techniques to generate speech acoustics resembling the voice of a genuine user and, hence, gain fraudulent access to the system. To prevent this, a number of anti-spoofing countermeasures have been developed for detecting these high technology attacks. However, the detection of previously unforeseen spoofing attacks remains challenging. To address this issue, in this work we perform an extensive empirical investigation on the speech features and back-end classifiers providing the best overall performance for an antispoofing system based on a deep learning framework. In this architecture, a deep neural network is used to extract a single identity spoofing vector per utterance from the speech features. Then, the extracted vectors are passed to a classifier in order to make the final detection decision. Experimental evaluation is carried out on the standard ASVSpoof2015 data corpus. The results show that classical FBANK features and Linear Discriminant Analysis (LDA) obtain the best performance for the proposed system.