Deep MOS Predictor for Synthetic Speech Using Cluster-Based Modeling

Understand

While deep learning has made impressive progress in speech synthesis and voice conversion, the assessment of the synthesized speech is still carried out by human participants.

• Several recent papers have proposed deep-learning-based assessment models and shown the potential to automate the speech quality assessment.
• To improve the previously proposed assessment model, MOSNet, we propose three models using cluster-based modeling methods: using a global quality token (GQT) layer, using an Encoding Layer, and using both of them.
• We perform experiments using the evaluation results of the Voice Conversion Challenge 2018 to predict the mean opinion score of synthesized speech and similarity score between synthesized speech and reference speech.