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This paper introduces a novel objective function for quality mean opinionscore (MOS) prediction of unseen speech synthesis systems. The proposedfunction measures the similarity of relative positions of predicted MOS values,in a mini-batch, rather than the actual MOS values. That is the partial ranksimilarity is measured (PRS) rather than the individual MOS values as with theL1 loss. Our experiments on out-of-domain speech synthesis systems demonstratethat the PRS outperforms L1 loss in zero-shot and semi-supervised settings,exhibiting stronger correlation with ground truth. These findings highlight theimportance of considering rank order, as done by PRS, when training MOSprediction models. We also argue that mean squared error and linear correlationcoefficient metrics may be unreliable for evaluating MOS prediction models. Inconclusion, PRS-trained models provide a robust framework for evaluating speechquality and offer insights for developing high-quality speech synthesissystems. Code and models are available atgithub.com/nii-yamagishilab/partial_rank_similarity/

Partial Rank Similarity Minimization Method for Quality MOS Prediction of Unseen Speech Synthesis Systems in Zero-Shot and Semi-supervised setting