  Thanks to recent advances in deep learning, sophisticated generation tools
exist, nowadays, that produce extremely realistic synthetic speech. However,
malicious uses of such tools are possible and likely, posing a serious threat
to our society. Hence, synthetic voice detection has become a pressing research
topic, and a large variety of detection methods have been recently proposed.
Unfortunately, they hardly generalize to synthetic audios generated by tools
never seen in the training phase, which makes them unfit to face real-world
scenarios. In this work, we aim at overcoming this issue by proposing a new
detection approach that leverages only the biometric characteristics of the
speaker, with no reference to specific manipulations. Since the detector is
trained only on real data, generalization is automatically ensured. The
proposed approach can be implemented based on off-the-shelf speaker
verification tools. We test several such solutions on three popular test sets,
obtaining good performance, high generalization ability, and high robustness to
audio impairment.
