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