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Several studies have proposed deep-learning-based models to predict the meanopinion score (MOS) of synthesized speech, showing the possibility of replacinghuman raters. However, inter- and intra-rater variability in MOSs makes it hardto ensure the high performance of the models. In this paper, we propose amulti-task learning (MTL) method to improve the performance of a MOS predictionmodel using the following two auxiliary tasks: spoofing detection (SD) andspoofing type classification (STC). Besides, we use the focal loss to maximizethe synergy between SD and STC for MOS prediction. Experiments using the MOSevaluation results of the Voice Conversion Challenge 2018 show that proposedMTL with two auxiliary tasks improves MOS prediction. Our proposed modelachieves up to 11.6% relative improvement in performance over the baselinemodel.

Neural MOS Prediction for Synthesized Speech Using Multi-Task Learning With Spoofing Detection and Spoofing Type Classification