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We present MooseNet, a trainable speech metric that predicts the listeners'Mean Opinion Score (MOS). We propose a novel approach where the ProbabilisticLinear Discriminative Analysis (PLDA) generative model is used on top of anembedding obtained from a self-supervised learning (SSL) neural network (NN)model. We show that PLDA works well with a non-finetuned SSL model when trainedonly on 136 utterances (ca. one minute training time) and that PLDAconsistently improves various neural MOS prediction models, evenstate-of-the-art models with task-specific fine-tuning. Our ablation studyshows PLDA training superiority over SSL model fine-tuning in a low-resourcescenario. We also improve SSL model fine-tuning using a convenient optimizerchoice and additional contrastive and multi-task training objectives. Thefine-tuned MooseNet NN with the PLDA module achieves the best results,surpassing the SSL baseline on the VoiceMOS Challenge data.

MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module