Subband fusion of complex spectrogram for fake speech detection

Abstract

The phase information was shown useful in fake speech detection. However, the most common reason why phase-based features are not widely used is phase wrapping. This makes the original phase hard to model directly. Therefore, it remains a challenge how to utilize the phase information effectively. To address this issue, this paper proposes a novel subband fusion of the complex spectrogram method for fake speech detection. The complex spectrogram is used as the input feature, containing both amplitude and phase spectrogram. In addition, subbands of the complex spectrogram are modeled separately. The idea is motivated by the fact that each frequency band has a different effect on the fake speech detection task. Finally, to make full use of the subbands, the subband results are fused. Experimental results on the ASVspoof 2019 LA dataset show that our proposed system achieves an equal error rate (EER) of 0.68% and a minimum tandem detection cost function (min t-DCF) of 0.0224.