Resource-Efficient Fine-Tuning Strategies for Automatic MOS Prediction
  in Text-to-Speech for Low-Resource Languages
Phat Do Matt Coler Jelske Dijkstra Esther Klabbers
We train a MOS prediction model based on wav2vec 2.0 using the open-access
data sets BVCC and SOMOS. Our test with neural TTS data in the low-resource
language (LRL) West Frisian shows that pre-training on BVCC before fine-tuning
on SOMOS leads to the best accuracy for both fine-tuned and zero-shot
prediction. Further fine-tuning experiments show that using more than 30
percent of the total data does not lead to significant improvements. In
addition, fine-tuning with data from a single listener shows promising
system-level accuracy, supporting the viability of one-participant pilot tests.
These findings can all assist the resource-conscious development of TTS for
LRLs by progressing towards better zero-shot MOS prediction and informing the
design of listening tests, especially in early-stage evaluation.