Synthetic Speech Detection Using Neural Networks

Abstract

Computer generated speech has improved drastically due to advancements in voice synthesis using deep learning techniques. The latest speech synthesizers achieve such high level of naturalness that humans have difficulty distinguishing real speech from computer generated speech. These technologies allow any person to train a synthesizer with a target voice, creating a model that is able to reproduce someone’s voice with high fidelity. This technology can be used in several legit commercial applications (e.g. call centres) as well as criminal activities, such as the impersonation of someone’s voice.In this paper, we analyze how synthetic speech is generated and propose deep learning methodologies to detect such synthesized utterances. Using a large dataset containing both synthetic and real speech, we analyzed the performance of the latest deep learning models in the classification of such utterances. Our proposed model achieves up to 92.00% accuracy in detecting unseen synthetic speech, which is a significant improvement from human performance (65.7%).