  We present the Voice Conversion Challenge 2018, designed as a follow up to
the 2016 edition with the aim of providing a common framework for evaluating
and comparing different state-of-the-art voice conversion (VC) systems. The
objective of the challenge was to perform speaker conversion (i.e. transform
the vocal identity) of a source speaker to a target speaker while maintaining
linguistic information. As an update to the previous challenge, we considered
both parallel and non-parallel data to form the Hub and Spoke tasks,
respectively. A total of 23 teams from around the world submitted their
systems, 11 of them additionally participated in the optional Spoke task. A
large-scale crowdsourced perceptual evaluation was then carried out to rate the
submitted converted speech in terms of naturalness and similarity to the target
speaker identity. In this paper, we present a brief summary of the
state-of-the-art techniques for VC, followed by a detailed explanation of the
challenge tasks and the results that were obtained.
