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

Voice conversion (VC) is a significant aspect of artificial intelligence. It is the study of how to convert one�s voice to sound like that of another without changing the linguistic content. Voice conversion belongs to a general technical field of speech synthesis, which converts text to speech or changes the properties of speech, for example, voice identity, emotion, and accents. 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 talk, 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 present 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.