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An effective approach to automatically predict the subjective rating forsynthetic speech is to train on a listening test dataset with human-annotatedscores. Although each speech sample in the dataset is rated by severallisteners, most previous works only used the mean score as the training target.In this work, we present LDNet, a unified framework for mean opinion score(MOS) prediction that predicts the listener-wise perceived quality given theinput speech and the listener identity. We reflect recent advances in LDmodeling, including design choices of the model architecture, and propose twoinference methods that provide more stable results and efficient computation.We conduct systematic experiments on the voice conversion challenge (VCC) 2018benchmark and a newly collected large-scale MOS dataset, providing an in-depthanalysis of the proposed framework. Results show that the mean listenerinference method is a better way to utilize the mean scores, whoseeffectiveness is more obvious when having more ratings per sample.

LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech