  In this paper, we present a new objective prediction model for synthetic
speech naturalness. It can be used to evaluate Text-To-Speech or Voice
Conversion systems and works language independently. The model is trained
end-to-end and based on a CNN-LSTM network that previously showed to give good
results for speech quality estimation. We trained and tested the model on 16
different datasets, such as from the Blizzard Challenge and the Voice
Conversion Challenge. Further, we show that the reliability of deep
learning-based naturalness prediction can be improved by transfer learning from
speech quality prediction models that are trained on objective POLQA scores.
The proposed model is made publicly available and can, for example, be used to
evaluate different TTS system configurations.
