  Mean opinion score (MOS) is a popular subjective metric to assess the quality
of synthesized speech, and usually involves multiple human judges to evaluate
each speech utterance. To reduce the labor cost in MOS test, multiple methods
have been proposed to automatically predict MOS scores. To our knowledge, for a
speech utterance, all previous works only used the average of multiple scores
from different judges as the training target and discarded the score of each
individual judge, which did not well exploit the precious MOS training data. In
this paper, we propose MBNet, a MOS predictor with a mean subnet and a bias
subnet to better utilize every judge score in MOS datasets, where the mean
subnet is used to predict the mean score of each utterance similar to that in
previous works, and the bias subnet to predict the bias score (the difference
between the mean score and each individual judge score) and capture the
personal preference of individual judges. Experiments show that compared with
MOSNet baseline that only leverages mean score for training, MBNet improves the
system-level spearmans rank correlation co-efficient (SRCC) by 2.9% on VCC 2018
dataset and 6.7% on VCC 2016 dataset.
