All-for-One and One-For-All: Deep learning-based feature fusion for
  Synthetic Speech Detection
Daniele Mari Davide Salvi Paolo Bestagini Simone Milani
Recent advances in deep learning and computer vision have made the synthesis
and counterfeiting of multimedia content more accessible than ever, leading to
possible threats and dangers from malicious users. In the audio field, we are
witnessing the growth of speech deepfake generation techniques, which solicit
the development of synthetic speech detection algorithms to counter possible
mischievous uses such as frauds or identity thefts. In this paper, we consider
three different feature sets proposed in the literature for the synthetic
speech detection task and present a model that fuses them, achieving overall
better performances with respect to the state-of-the-art solutions. The system
was tested on different scenarios and datasets to prove its robustness to
anti-forensic attacks and its generalization capabilities.