Text-To-Speech (TTS) prosody transfer models can generate varied prosodic
renditions, for the same text, by conditioning on a reference utterance. These
models are trained with a reference that is identical to the target utterance.
But when the reference utterance differs from the target text, as in cross-text
prosody transfer, these models struggle to separate prosody from text,
resulting in reduced perceived naturalness. To address this, we propose a
Human-in-the-Loop (HitL) approach. HitL users adjust salient correlates of
prosody to make the prosody more appropriate for the target text, while
maintaining the overall reference prosodic effect. Human adjusted renditions
maintain the reference prosody while being rated as more appropriate for the
target text $57.8\%$ of the time. Our analysis suggests that limited user
effort suffices for these improvements, and that closeness in the latent
reference space is not a reliable prosodic similarity metric for the cross-text
condition.

通过使用人工调整显著的韵律相关因素，使得生成语音更适合目标文本，同时保持整体参考音调效果，可以提高跨文本韵律转换模型的感知自然度约 57.8％，分析结果显示，有限的用户努力就足够实现这些改进，并且潜在的参考空间接近性不是可靠的跨文本韵律相似度度量。