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

Understand

An effective approach to automatically predict the subjective rating for synthetic speech is to train on a listening test dataset with human-annotated scores.

• Although each speech sample in the dataset is rated by several listeners, 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 the input speech and the listener identity.
• We reflect recent advances in LD modeling, including design choices of the model architecture, and propose two inference methods that provide more stable results and efficient computation.