CONTEXT-AWARE MASK PREDICTION NETWORK 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 and 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), which avoids the unnatural phenomenon caused by cut-copy-paste operation in the traditional method and can synthesize a new word not appearing in the transcript. Besides, three text-based speech editing operations based on CampNet are designed: deletion, replacement, and insertion. These operations can comprehensively cover different kinds of situations that text-based speech editing can face. The subjective and objective experiments on VCTK and LibriTTS data sets show that the speech editing results based on CampNet are better than TTS technology, manual editing, and VoCo method (the combination of speech synthesis and speech conversion). We also conducted detailed ablation experiments to explore the effect of the CampNet structure on its performance. Examples of generated speech can be found at https://hairuo55.github.io/CampNet-demo.