  The text-based speech editor allows the editing of speech through intuitive
cutting, copying, and pasting operations to speed up the process of editing
speech. However, the major drawback of current systems is that edited speech
often sounds unnatural due to cut-copy-paste operation. In addition, it is not
obvious how to synthesize records according to a new word not appearing in the
transcript. This paper proposes a novel end-to-end text-based speech editing
method called context-aware mask prediction network (CampNet). The model can
simulate the text-based speech editing process by randomly masking part of
speech and then predicting the masked region by sensing the speech context. It
can solve unnatural prosody in the edited region and synthesize the speech
corresponding to the unseen words in the transcript. Secondly, for the possible
operation of text-based speech editing, we design three text-based operations
based on CampNet: deletion, insertion, and replacement. These operations can
cover various situations of speech editing. Thirdly, to synthesize the speech
corresponding to long text in insertion and replacement operations, a
word-level autoregressive generation method is proposed. Fourthly, we propose a
speaker adaptation method using only one sentence for CampNet and explore the
ability of few-shot learning based on CampNet, which provides a new idea for
speech forgery tasks. The subjective and objective experiments on VCTK and
LibriTTS datasets show that the speech editing results based on CampNet are
better than TTS technology, manual editing, and VoCo method. We also conduct
detailed ablation experiments to explore the effect of the CampNet structure on
its performance. Finally, the experiment shows that speaker adaptation with
only one sentence can further improve the naturalness of speech. Examples of
generated speech can be found at https://hairuo55.github.io/CampNet.
