  MOS (Mean Opinion Score) is a subjective method used for the evaluation of a
system's quality. Telecommunications (for voice and video), and speech
synthesis systems (for generated speech) are a few of the many applications of
the method. While MOS tests are widely accepted, they are time-consuming and
costly since human input is required. In addition, since the systems and
subjects of the tests differ, the results are not really comparable. On the
other hand, a large number of previous tests allow us to train machine learning
models that are capable of predicting MOS value. By automatically predicting
MOS values, both the aforementioned issues can be resolved.
  The present work introduces data-, training- and post-training specific
improvements to a previous self-supervised learning-based MOS prediction model.
We used a wav2vec 2.0 model pre-trained on LibriSpeech, extended with LSTM and
non-linear dense layers. We introduced transfer learning, target data
preprocessing a two- and three-phase training method with different batch
formulations, dropout accumulation (for larger batch sizes) and quantization of
the predictions.
  The methods are evaluated using the shared synthetic speech dataset of the
first Voice MOS challenge.
