While modern Text-to-Speech (TTS) systems can produce natural-sounding speech, they remain unable to reproduce the full diversity found in natural speech data. We consider the distribution of all possible real speech samples that could be generated by these speakers alongside the distribution of all synthetic samples that could be generated for the same set of speakers, using a particular TTS system. We set out to quantify the distance between real and synthetic speech via a range of utterance-level statistics related to properties of the speaker, speech prosody and acoustic environment. Differences in the distribution of these statistics are evaluated using the Wasserstein distance. We reduce these distances by providing ground-truth values at generation time, and quantify the improvements to the overall distribution distance, approximated using an automatic speech recognition system. Our best system achieves a 10\% reduction in distribution distance.

翻译：虽然现代文本转语音（TTS）系统能够生成自然度较高的语音，但仍无法复现自然语音数据中的全部多样性。我们考虑了一组特定说话者可能产生的所有真实语音样本的分布，以及使用特定TTS系统为同一组说话者生成的所有合成样本的分布。我们通过一系列与说话者特征、语音韵律及声学环境相关的语句级统计量，量化真实语音与合成语音之间的距离。采用Wasserstein距离评估这些统计量分布的差异。通过生成阶段提供真实值来缩减这些距离，并利用自动语音识别系统近似量化整体分布距离的改进效果。我们的最优系统实现了分布距离10%的缩减。