With the advancements in deep learning and other techniques, synthetic speech is getting closer to a natural sounding voice. Some of the state-of-art technologies achieve such a high level of naturalness that even humans have difficulties distinguishing real speech from computer generated speech. Moreover, these technologies allow a person to train a speech synthesizer with a target voice, creating a model that is able to reproduce someone's voice with high fidelity.
With this research, we thoroughly analyze how synthetic speech is generated and propose deep learning methodologies to detect such synthesized utterances. We first collected a significant amount of real and synthetic utterances to create the Fake or Real (FoR) dataset. Then, we analyzed the performance of the latest deep learning models in the classification of such utterances. Our proposed model achieves 99.86% accuracy in synthetic speech detection, which is a significant improvement from a human performance (65.7%)
