  Modern text-to-speech (TTS) and voice conversion (VC) systems produce natural
sounding speech that questions the security of automatic speaker verification
(ASV). This makes detection of such synthetic speech very important to
safeguard ASV systems from unauthorized access. Most of the existing spoofing
countermeasures perform well when the nature of the attacks is made known to
the system during training. However, their performance degrades in face of
unseen nature of attacks. In comparison to the synthetic speech created by a
wide range of TTS and VC methods, genuine speech has a more consistent
distribution. We believe that the difference between the distribution of
synthetic and genuine speech is an important discriminative feature between the
two classes. In this regard, we propose a novel method referred to as feature
genuinization that learns a transformer with convolutional neural network (CNN)
using the characteristics of only genuine speech. We then use this
genuinization transformer with a light CNN classifier. The ASVspoof 2019
logical access corpus is used to evaluate the proposed method. The studies show
that the proposed feature genuinization based LCNN system outperforms other
state-of-the-art spoofing countermeasures, depicting its effectiveness for
detection of synthetic speech attacks.
