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.
翻译:语音转换的现有客观评价衡量标准(VC)并不总是与人类感知相联系,因此,用这种标准培训VC模式可能无法有效地改善转换的语音的自然性和相似性。在本文中,我们提出深层次的学习评估模型,以预测转换的语音的人类评级。我们采用进化和经常性神经网络模型,以构建一个称为MOSNet的中值意见评分(MOS)预测器。拟议模型在2018年语音转换挑战(VCC)大规模监听测试结果中进行测试。实验结果显示,拟议的MOSNet的预测分数与系统一级的人类MOS评级高度相关,同时与全方位的人类MOS评级相当相关。与此同时,我们修改了MOSNet,以预测相似性评分,初步结果显示,预测的分数也与人类评级相当相关。这些结果证实,拟议的模型可以用作计算评价员,以测量VC系统的MOS,以减少对昂贵的人评级的需要。