Arbitrary Discrete Fourier Analysis and Its Application in Replayed Speech Detection

In this paper, a signal analysis concept is derived when revisiting how a specific frequency component in spectrum is analyzed in Fourier analysis. Three signal analysis methods are then developed based on the derived concept, namely Arbitrary Discrete Fourier Analysis (ADFA), Mel-scale Discrete Fourier Analysis (MDFA), and constant Q Analysis (CQA). I validate the effectiveness of these three signal analysis methods by testing their performance on a replayed speech detection benchmark (i.e., the ASVspoof 2019 Physical Access) along with a state-of-the-art model. Experimental results show that the performance of these three signal analysis methods is comparable to the best reported systems. At the same time, it is show that the computation time of the developed method CQA is much shorter than the convention method constant Q Transform, which is commonly used in spoofed and fake speech detection and music processing.

A Study of Using Cepstrogram for Countermeasure Against Replay Attacks

In this paper, we investigate the properties of the cepstrogram and demonstrate its effectiveness as a powerful feature for countermeasure against replay attacks. Cepstrum analysis of replay attacks suggests that crucial information for anti-spoofing against replay attacks may retain in the cepstrogram. Experimental results on the ASVspoof 2019 physical access (PA) database demonstrate that, compared with other features, the cepstrogram dominates in both single and fusion systems when building countermeasures against replay attacks. Our LCNN-based single and fusion systems with the cepstrogram feature outperform the corresponding LCNN-based systems without using the cepstrogram feature and several state-of-the-art (SOTA) single and fusion systems in the literature.