We present MooseNet, a trainable speech metric that predictsthe listeners’ Mean Opinion Score (MOS). We propose a novelapproach where the Probabilistic Linear Discriminative Analysis (PLDA) generative model is used on top of an embedding obtained from a self-supervised learning (SSL) neuralnetwork (NN) model. We show that PLDA works well witha non-finetuned SSL model when trained only on 136 utterances (ca. one minute training time) and that PLDA consistentlyimproves various neural MOS prediction models, even stateof-the-art models with task-specific fine-tuning. Our ablationstudy shows PLDA training superiority over SSL model fine-tuning in a low-resource scenario. We also improve SSL modelfine-tuning using a convenient optimizer choice and additionalcontrastive and multi-task training objectives. The fine-tunedMooseNet NN with the PLDA module achieves the best results,surpassing the SSL baseline on the VoiceMOS Challenge data.