  Stutter removal is an essential scenario in the field of speech editing.
However, when the speech recording contains stutters, the existing text-based
speech editing approaches still suffer from: 1) the over-smoothing problem in
the edited speech; 2) lack of robustness due to the noise introduced by
stutter; 3) to remove the stutters, users are required to determine the edited
region manually. To tackle the challenges in stutter removal, we propose
FluentSpeech, a stutter-oriented automatic speech editing model. Specifically,
1) we propose a context-aware diffusion model that iteratively refines the
modified mel-spectrogram with the guidance of context features; 2) we introduce
a stutter predictor module to inject the stutter information into the hidden
sequence; 3) we also propose a stutter-oriented automatic speech editing (SASE)
dataset that contains spontaneous speech recordings with time-aligned stutter
labels to train the automatic stutter localization model. Experimental results
on VCTK and LibriTTS datasets demonstrate that our model achieves
state-of-the-art performance on speech editing. Further experiments on our SASE
dataset show that FluentSpeech can effectively improve the fluency of
stuttering speech in terms of objective and subjective metrics. Code and audio
samples can be found at https://github.com/Zain-Jiang/Speech-Editing-Toolkit.
