  Current state-of-the-art methods for automatic synthetic speech evaluation
are based on MOS prediction neural models. Such MOS prediction models include
MOSNet and LDNet that use spectral features as input, and SSL-MOS that relies
on a pretrained self-supervised learning model that directly uses the speech
signal as input. In modern high-quality neural TTS systems, prosodic
appropriateness with regard to the spoken content is a decisive factor for
speech naturalness. For this reason, we propose to include prosodic and
linguistic features as additional inputs in MOS prediction systems, and
evaluate their impact on the prediction outcome. We consider phoneme level F0
and duration features as prosodic inputs, as well as Tacotron encoder outputs,
POS tags and BERT embeddings as higher-level linguistic inputs. All MOS
prediction systems are trained on SOMOS, a neural TTS-only dataset with
crowdsourced naturalness MOS evaluations. Results show that the proposed
additional features are beneficial in the MOS prediction task, by improving the
predicted MOS scores' correlation with the ground truths, both at
utterance-level and system-level predictions.
