  Mean opinion score (MOS) is a typical subjective evaluation metric for speech
synthesis systems. Since collecting MOS is time-consuming, it would be
desirable if there are accurate MOS prediction models for automatic evaluation.
In this work, we propose DDOS, a novel MOS prediction model. DDOS utilizes
domain adaptive pre-training to further pre-train self-supervised learning
models on synthetic speech. And a proposed module is added to model the opinion
score distribution of each utterance. With the proposed components, DDOS
outperforms previous works on BVCC dataset. And the zero shot transfer result
on BC2019 dataset is significantly improved. DDOS also wins second place in
Interspeech 2022 VoiceMOS challenge in terms of system-level score.
