The Historical Review and Current Trends in Speech Synthesis by Bibliometric Approach

Deng, Guang-Feng; Tsai, Cheng‐Hung; Ku, Tsun

doi:10.1007/978-981-13-3648-5_252

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Deep Learning-based Tts Technology1

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2021

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“…There have been some reviews on TTS. For example, Deng et al [43] analyzed the number of documents and citations of TTS papers from 1992 to 2017, aiming to help researchers understand the development trend of TTS. Aroon and Dhonde [5] reviewed SPSS methods based on HMM.…”

Section: Deep Learning-based Tts Technologymentioning

confidence: 99%

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

Mu¹,

Yang²,

Dong³

2021

Preprint

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As an indispensable part of modern humancomputer 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 frontend, 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 opensource 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.

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Section: Deep Learning-based Tts Technologymentioning

confidence: 99%