A Watermark Challenge: Synthetic Speech Detection

Synthetic audio signal generation is an easier task with the help of open-source software and deep learning tools. These freely available tools are maliciously used and it negatively impacts society. To overcome this problem, we made an attempt to develop a synthetic audio signal detector. This work measures statistical and entropy features on discrete wavelength transform (DWT) transformed input audio signal. These features are trained and tested using supervised classification techniques. The proposed work is validated on a publicly available synthetic audio database. The accuracy of the proposed work is 99.0% and is compared with the state-of-the-art works validating superiority over other existing methods.