Syn-Att: Synthetic Speech Attribution via Semi-Supervised Unknown
  Multi-Class Ensemble of CNNs
Md Awsafur Rahman Bishmoy Paul Najibul Haque Sarker Zaber Ibn Abdul Hakim Shaikh Anowarul Fattah Mohammad Saquib
With the huge technological advances introduced by deep learning in audio &
speech processing, many novel synthetic speech techniques achieved incredible
realistic results. As these methods generate realistic fake human voices, they
can be used in malicious acts such as people imitation, fake news, spreading,
spoofing, media manipulations, etc. Hence, the ability to detect synthetic or
natural speech has become an urgent necessity. Moreover, being able to tell
which algorithm has been used to generate a synthetic speech track can be of
preeminent importance to track down the culprit. In this paper, a novel
strategy is proposed to attribute a synthetic speech track to the generator
that is used to synthesize it. The proposed detector transforms the audio into
log-mel spectrogram, extracts features using CNN, and classifies it between
five known and unknown algorithms, utilizing semi-supervision and ensemble to
improve its robustness and generalizability significantly. The proposed
detector is validated on two evaluation datasets consisting of a total of
18,000 weakly perturbed (Eval 1) & 10,000 strongly perturbed (Eval 2) synthetic
speeches. The proposed method outperforms other top teams in accuracy by 12-13%
on Eval 2 and 1-2% on Eval 1, in the IEEE SP Cup challenge at ICASSP 2022.