  We present the UTokyo-SaruLab mean opinion score (MOS) prediction system
submitted to VoiceMOS Challenge 2022. The challenge is to predict the MOS
values of speech samples collected from previous Blizzard Challenges and Voice
Conversion Challenges for two tracks: a main track for in-domain prediction and
an out-of-domain (OOD) track for which there is less labeled data from
different listening tests. Our system is based on ensemble learning of strong
and weak learners. Strong learners incorporate several improvements to the
previous fine-tuning models of self-supervised learning (SSL) models, while
weak learners use basic machine-learning methods to predict scores from SSL
features. In the Challenge, our system had the highest score on several metrics
for both the main and OOD tracks. In addition, we conducted ablation studies to
investigate the effectiveness of our proposed methods.
