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}.

翻译：基于文本的语音编辑技术旨在让用户通过修改输入文本转录而非直接编辑音频来调整输出音频。尽管基于神经网络的文本语音编辑技术取得了显著进展，现有技术主要聚焦于减少编辑区域生成语音片段与参考目标的差异，忽略了其与上下文及原始话语在局部和整体层面的流畅性。为保持语音流畅性，我们提出了一种名为FluentEditor的流畅语音编辑模型，通过在文本语音编辑训练中引入流畅性感知训练准则。具体而言，**声学一致性约束**旨在使编辑区域与其相邻声学片段之间的过渡平滑且与真实数据一致，而**韵律一致性约束**则力求确保编辑区域内的韵律属性与原始话语的整体风格保持一致。在VCTK数据集上的主观和客观实验结果表明，我们的FluentEditor在自然度和流畅性方面均优于所有先进基线模型。音频样本和代码已开源至\url{https://github.com/Ai-S2-Lab/FluentEditor}。