Combining wav2vec2 semantic features, BiVocoder acoustic features, and listener IDs in a multi-task MOS predictor improves system-level naturalness ranking on BVCC while remaining competitive on out-of-domain BC2019.
We can see that removing the semantic module resulted in the degradation of all the metrics on both datasets, indicating the importance of semantic repre- sentations from SSL model
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SAMOS: A Neural MOS Prediction Model Leveraging Semantic Representations and Acoustic Features
Combining wav2vec2 semantic features, BiVocoder acoustic features, and listener IDs in a multi-task MOS predictor improves system-level naturalness ranking on BVCC while remaining competitive on out-of-domain BC2019.