Significance of Subband Features for Synthetic Speech Detection
Jichen Yang, Rohan Kumar Das, Haizhou Li · IEEE Transactions on Information Forensics and Security · 2019
In text-to-speech or voice conversion based synthetic speech detection, it is a common practice that spectral information over the entire frequency band is used for feature representation. We propose a new method, referred to as subband transform, that characterizes the signals by subband. It is found that subband transform captures the artifacts in synthetic speech more effectively than full band transform. We propose equal subband transform, octave subband transform, and mel subband transform for three novel features, namely, constant-Q equal subband transform (CQ-EST), constant-Q octave subband transform (CQ-OST) and discrete Fourier mel subband transform (DF-MST). We evaluate the three features on the ASVspoof 2015, noisy ASVspoof 2015 and ASVspoof 2019 logical access corpora. The experiments show that the proposed CQ-EST feature achieves an average equal error rate of 0.056% on ASVspoof 2015 evaluation set. The study observes that the features based on subband transform outperform those based on full band transform under both clean and noisy conditions. In addition, the tandem detection cost function of CQ-OST can reach 0.188 on ASVspoof 2019 logical access evaluation set.