Voice Cloning & Deep Fake Audio Detection Using Deep Learning

Abstract

Voice cloning and fake audio detection are two critical areas in the field of audio processing and artificial intelligence. Voice cloning aims to synthesize speech with the characteristics of a target speaker, enabling applications such as virtual assistants and personalized voice interfaces. On the other hand, fake audio detection involves identifying manipulated or synthetic audio content, particularly in the context of deep fake technology, to combat misinformation and preserve audio authenticity. In this report, we present a comprehensive overview of voice cloning and fake audio detection techniques, including data collection, preprocessing, feature extraction, model architectures, and evaluation methodologies. We explore state-of-the-art algorithms and methodologies employed in each domain, along with practical applications and future research directions. Our analysis highlights the importance of advancing voice cloning and fake audio detection technologies to address emerging challenges in audio synthesis, manipulation, and verification.