  The Voice Conversion Challenge 2020 is the third edition under its flagship
that promotes intra-lingual semiparallel and cross-lingual voice conversion
(VC). While the primary evaluation of the challenge submissions was done
through crowd-sourced listening tests, we also performed an objective
assessment of the submitted systems. The aim of the objective assessment is to
provide complementary performance analysis that may be more beneficial than the
time-consuming listening tests. In this study, we examined five types of
objective assessments using automatic speaker verification (ASV), neural
speaker embeddings, spoofing countermeasures, predicted mean opinion scores
(MOS), and automatic speech recognition (ASR). Each of these objective measures
assesses the VC output along different aspects. We observed that the
correlations of these objective assessments with the subjective results were
high for ASV, neural speaker embedding, and ASR, which makes them more
influential for predicting subjective test results. In addition, we performed
spoofing assessments on the submitted systems and identified some of the VC
methods showing a potentially high security risk.
