Generating versatile and appropriate synthetic speech requires control over
the output expression separate from the spoken text. Important non-textual
speech variation is seldom annotated, in which case output control must be
learned in an unsupervised fashion. In this paper, we perform an in-depth study
of methods for unsupervised learning of control in statistical speech
synthesis. For example, we show that popular unsupervised training heuristics
can be interpreted as variational inference in certain autoencoder models. We
additionally connect these models to VQ-VAEs, another, recently-proposed class
of deep variational autoencoders, which we show can be derived from a very
similar mathematical argument. The implications of these new probabilistic
interpretations are discussed. We illustrate the utility of the various
approaches with an application to acoustic modelling for emotional speech
synthesis, where the unsupervised methods for learning expression control
(without access to emotional labels) are found to give results that in many
aspects match or surpass the previous best supervised approach.

本文针对非文字语音变化很少被注释的情况，研究了无监督学习控制统计语音合成的方法，包括将无监督训练试探法解释为某些自编码器模型中的变分推理等方式。研究发现，这些新的概率诠释具有重要含义，并将几种方法应用于情感语音合成的音频建模，发现无监督学习的表达控制方法（无需情感标签）在很多方面可以与之前的最佳监督方案匹配或超越之。