Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space. Aiming to improve those assumptions, Normalizing Flows and Diffusion Probabilistic Models were recently proposed as alternatives. In this paper, we compare traditional L1/L2-based approaches to diffusion and flow-based approaches for the tasks of prosody and mel-spectrogram prediction for text-to-speech synthesis. We use a prosody model to generate log-f0 and duration features, which are used to condition an acoustic model that generates mel-spectrograms. Experimental results demonstrate that the flow-based model achieves the best performance for spectrogram prediction, improving over equivalent diffusion and L1 models. Meanwhile, both diffusion and flow-based prosody predictors result in significant improvements over a typical L2-trained prosody models.

翻译：神经文本到语音系统通常基于L1/L2损失函数进行优化，这对目标数据空间的分布做出了较强假设。为改善这些假设，归一化流和扩散概率模型最近被提出作为替代方案。本文针对文本到语音合成中的韵律预测和梅尔频谱预测任务，比较了传统L1/L2方法与基于扩散和流的方法。我们使用韵律模型生成对数基频和时长特征，并以此作为条件输入生成梅尔频谱的声学模型。实验结果表明，基于流的模型在频谱预测上表现最佳，优于等效的扩散模型和L1模型。同时，基于扩散和流的韵律预测器相较于典型L2训练模型均带来显著改进。