  The constant Q transform (CQT) has been shown to be one of the most effective
speech signal pre-transforms to facilitate synthetic speech detection, followed
by either hand-crafted (subband) constant Q cepstral coefficient (CQCC) feature
extraction and a back-end binary classifier, or a deep neural network (DNN)
directly for further feature extraction and classification. Despite the rich
literature on such a pipeline, we show in this paper that the pre-transform and
hand-crafted features could simply be replaced by end-to-end DNNs.
Specifically, we experimentally verify that by only using standard components,
a light-weight neural network could outperform the state-of-the-art methods for
the ASVspoof2019 challenge. The proposed model is termed Time-domain Synthetic
Speech Detection Net (TSSDNet), having ResNet- or Inception-style structures.
We further demonstrate that the proposed models also have attractive
generalization capability. Trained on ASVspoof2019, they could achieve
promising detection performance when tested on disjoint ASVspoof2015,
significantly better than the existing cross-dataset results. This paper
reveals the great potential of end-to-end DNNs for synthetic speech detection,
without hand-crafted features.

Audio and Speech Processing