Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features. (arXiv:2211.00342v1 [cs.SD])

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 …

arxiv features mos prediction speech text text-to-speech