A survey of AI-generated voices and their detection
Organizations: Department of Computer Science and Engineering, University at Buffalo, Buffalo, New York, USA · Department of Linguistics, University at Buffalo, Buffalo, New York, USA
Abstract
The ability of artificial intelligence (AI) models to generate highly realistic human voices has advanced rapidly. These technologies power accessibility tools, virtual assistants and creative applications, but they also enable harmful uses, including impersonation, fraud and disinformation. Recent incidents of voice cloning scams targeting businesses and political leaders underscore the urgent need for robust safeguards. Unlike image and video deepfakes, the detection of synthetic voices poses unique challenges due to the complexity of phonetics, prosody and auditory perception. This survey offers a comprehensive overview of AI voice generation and detection methods, encompassing both the technical foundations and the latest state-of-the-art advances. This study also identifies key open challenges, benchmark resources and future directions to make this survey useful for future researchers.
Audio Deepfake Detection
Audio deepfake detection identifies synthetic, cloned, or manipulated speech and other audio to support authentication and media forensics.
See all 204 papers