MooseNet: A trainable metric for synthesized speech with plda backend

We present MooseNet, a trainable speech metric that predicts listeners' Mean Opinion Score (MOS). We report improvements to the challenge baselines using easy-to-use modeling techniques, which also scales for larger self-supervised learning (SSL) model. We present two models. The first model is a Neural Network (NN). As a second model, we propose a PLDA generative model on the top layers of the first NN model, which improves the pure NN model. Ensembles from our two models achieve the top 3 or 4 VoiceMOS leaderboard places on all system and utterance level metrics.