  Text-based speech editing (TSE) techniques are designed to enable users to
edit the output audio by modifying the input text transcript instead of the
audio itself. Despite much progress in neural network-based TSE techniques, the
current techniques have focused on reducing the difference between the
generated speech segment and the reference target in the editing region,
ignoring its local and global fluency in the context and original utterance. To
maintain the speech fluency, we propose a fluency speech editing model, termed
\textit{FluentEditor}, by considering fluency-aware training criterion in the
TSE training. Specifically, the \textit{acoustic consistency constraint} aims
to smooth the transition between the edited region and its neighboring acoustic
segments consistent with the ground truth, while the \textit{prosody
consistency constraint} seeks to ensure that the prosody attributes within the
edited regions remain consistent with the overall style of the original
utterance. The subjective and objective experimental results on VCTK
demonstrate that our \textit{FluentEditor} outperforms all advanced baselines
in terms of naturalness and fluency. The audio samples and code are available
at \url{https://github.com/Ai-S2-Lab/FluentEditor}.
