  The rapid spread of media content synthesis technology and the potentially
damaging impact of audio and video deepfakes on people's lives have raised the
need to implement systems able to detect these forgeries automatically. In this
work we present a novel approach for synthetic speech detection, exploiting the
combination of two high-level semantic properties of the human voice. On one
side, we focus on speaker identity cues and represent them as speaker
embeddings extracted using a state-of-the-art method for the automatic speaker
verification task. On the other side, voice prosody, intended as variations in
rhythm, pitch or accent in speech, is extracted through a specialized encoder.
We show that the combination of these two embeddings fed to a supervised binary
classifier allows the detection of deepfake speech generated with both
Text-to-Speech and Voice Conversion techniques. Our results show improvements
over the considered baselines, good generalization properties over multiple
datasets and robustness to audio compression.
