  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.
