Review of end-to-end speech synthesis technology based on deep learning

By Zhaoxi Mu, Xinyu Yang, Yizhuo Dong. 2021

32 citations recorded by the source corpus.

As an indispensable part of modern human-computer interaction system, speech synthesis technology helps users get the output of intelligent machine more easily and intuitively, thus has attracted more and more attention. Due to the limitations of high complexity and low efficiency of traditional speech synthesis technology, the current research focus is the deep learning-based end-to-end speech synthesis technology, which has more powerful modeling ability and a simpler pipeline. It mainly consists of three modules: text front-end, acoustic model, and vocoder. This paper reviews the research status of these three parts, and classifies and compares various methods according to their emphasis. Moreover, this paper also summarizes the open-source speech corpus of English, Chinese and other languages that can be used for speech synthesis tasks, and introduces some commonly used subjective and objective speech quality evaluation method. Finally, some attractive future research directions are pointed out.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Yi Ren
6. Olivier Chapelle
7. Yanqing Liu
8. Rongjie Huang

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, background): “While there are multiple survey papers on statistical parametric speech synthesis [27, 418, 351, 415] and neural TTS [325, 220, 300, 242, 115, 254, 236], a comprehensive survey on the basics and the recent developments of neural TTS is still necessary since the topics in this area are diverse and ev”