  Several studies have proposed deep-learning-based models to predict the mean
opinion score (MOS) of synthesized speech, showing the possibility of replacing
human raters. However, inter- and intra-rater variability in MOSs makes it hard
to ensure the high performance of the models. In this paper, we propose a
multi-task learning (MTL) method to improve the performance of a MOS prediction
model using the following two auxiliary tasks: spoofing detection (SD) and
spoofing type classification (STC). Besides, we use the focal loss to maximize
the synergy between SD and STC for MOS prediction. Experiments using the MOS
evaluation results of the Voice Conversion Challenge 2018 show that proposed
MTL with two auxiliary tasks improves MOS prediction. Our proposed model
achieves up to 11.6% relative improvement in performance over the baseline
model.
