Seybold Report ISSN: 1533-9211



Jyoti Seth1, Shobha Bhatt2

Vol 18, No 5 ( 2023 )   |  Licensing: CC 4.0   |   Pg no: 118-131   |   Published on: 10-05-2023

This review paper investigates advances in speech synthesis using deep learning approaches. For many years, speech synthesis has been an important topic of research, and with recent advances in deep learning, new ways to generating more natural-sounding speech have been proposed. The study presents an introduction of several deep learning approaches used for speech synthesis, such as Generative Adversarial Networks (GANs), WaveNet, and Tacotron,Deep Bidirectional Long-short term memory(DBLSTM). It also highlights the difficulties that researchers encounter, such as the availability of training data, model complexity, and evaluation criteria. Finally, the paper concludes with potential future avenues for deep learning-based speech synthesis research. Speech Synthesis, WaveNet, Tacotron, GAN(Generative Adversarial Networks),Deep Bidirectional Long-short term memory(DBLSTM). researchers confront, and probable future avenues for study in this subject. The remaining part of the paper is structured as fol- lows.Section 2 describes the Literature Review . Section 3 describes an overview of speech synthesis in that we have discussed about the various advantages and challenges occurs during the research. Section 4 describes the various methods used for speech synthesis like WaveNet, Tacotron , Generative Adversarial Networks(GAN),Deep Bidirectional Long-short term memory(DBLSTM).Section 5 describes the Result and Discussion of different techniques followed by a comparative analysis of different techniques .Finally, conclusions and future work suggestions are presented in Section 6.


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