E$^{3}$TTS: End-to-End Text-Based Speech Editing TTS System and Its Applications

Abstract

Text-based speech editing aims at manipulating part of real audio by modifying the corresponding transcribed text, without being discernible by human auditory system. With the enhanced capability of neural Text-to-speech (TTS), researchers try to tackle speech editing problems with TTS methods. In this paper, we propose E $^{3}$ TTS, a.k.a. end-to-end text-based speech editing TTS system, which combines a text encoder, a speech encoder, and a joint net for speech synthesis and speech editing. E $^{3}$ TTS can insert, replace, and delete speech content at will, by manipulating the given text. Experiments show that our speech editing outperforms strong baselines on HiFiTTS and LibriTTS datasets, speakers of which are seen or unseen, respectively. Further, we introduce E $^{3}$ TTS into data augmentation for automatic speech recognition (ASR) to mitigate the data insufficiency problem in code-switching and named entity recognition scenarios ¹ . E $^{3}$ TTS retains the coherence and reality of the recorded audio compared to past data augmentation methods. The experimental results show significant performance improvements over baseline systems with traditional TTS-based data augmentation. The code and samples of the proposed speech editing model are available at this repository. ²