  The recent advancements in generative artificial speech models have made
possible the generation of highly realistic speech signals. At first, it seems
exciting to obtain these artificially synthesized signals such as speech clones
or deep fakes but if left unchecked, it may lead us to digital dystopia. One of
the primary focus in audio forensics is validating the authenticity of a
speech. Though some solutions are proposed for English speeches but the
detection of synthetic Hindi speeches have not gained much attention. Here, we
propose an approach for discrimination of AI synthesized Hindi speech from an
actual human speech. We have exploited the Bicoherence Phase, Bicoherence
Magnitude, Mel Frequency Cepstral Coefficient (MFCC), Delta Cepstral, and Delta
Square Cepstral as the discriminating features for machine learning models.
Also, we extend the study to using deep neural networks for extensive
experiments, specifically VGG16 and homemade CNN as the architecture models. We
obtained an accuracy of 99.83% with VGG16 and 99.99% with homemade CNN models.
