Existing objective evaluation metrics for voice conversion (VC) are not
always correlated with human perception. Therefore, training VC models with
such criteria may not effectively improve naturalness and similarity of
converted speech. In this paper, we propose deep learning-based assessment
models to predict human ratings of converted speech. We adopt the convolutional
and recurrent neural network models to build a mean opinion score (MOS)
predictor, termed as MOSNet. The proposed models are tested on large-scale
listening test results of the Voice Conversion Challenge (VCC) 2018.
Experimental results show that the predicted scores of the proposed MOSNet are
highly correlated with human MOS ratings at the system level while being fairly
correlated with human MOS ratings at the utterance level. Meanwhile, we have
modified MOSNet to predict the similarity scores, and the preliminary results
show that the predicted scores are also fairly correlated with human ratings.
These results confirm that the proposed models could be used as a computational
evaluator to measure the MOS of VC systems to reduce the need for expensive
human rating.

本文提出了基于深度学习的评估模型 MOSNet，用于预测转换语音的人类评分，实验结果表明其预测得分与人类得分高度相关，因此可以用作减少昂贵的人类评分需求的计算评估器。