  We present the first edition of the VoiceMOS Challenge, a scientific event
that aims to promote the study of automatic prediction of the mean opinion
score (MOS) of synthetic speech. This challenge drew 22 participating teams
from academia and industry who tried a variety of approaches to tackle the
problem of predicting human ratings of synthesized speech. The listening test
data for the main track of the challenge consisted of samples from 187
different text-to-speech and voice conversion systems spanning over a decade of
research, and the out-of-domain track consisted of data from more recent
systems rated in a separate listening test. Results of the challenge show the
effectiveness of fine-tuning self-supervised speech models for the MOS
prediction task, as well as the difficulty of predicting MOS ratings for unseen
speakers and listeners, and for unseen systems in the out-of-domain setting.
