CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing

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 first proposes a novel end-to-end text-based speech editing method called context-aware mask prediction network (CampNet), which can solve unnatural prosody in the edited region and synthesize the speech corresponding to the unseen words in the transcript. Secondly, to cover various situations of text-based speech editing, we design three text-based operations based on CampNet: deletion, insertion, and replacement. Thirdly, to synthesize the speech corresponding to long text, 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
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Examples of generated speech can be found at https://hairuo55.github.io/CampNet

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 editing for one-shot learning.