An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

Speaker identity is one of the important characteristics human speech. In voice conversion, we change speaker from to another, while keeping linguistic content unchanged. Voice conversion involves multiple speech processing techniques, such as analysis, spectral prosody characterization, and vocoding. With recent advances in theory practice, are now able produce human-like quality with high similarity. this article, provide a comprehensive overview state-of-the-art techniques their performance evaluation methods statistical approaches deep learning, discuss promise limitations. We will also report Conversion Challenges (VCC), current state technology, summary available resources for research.