Survey on Various Methods of Text to Speech Synthesis
By Desai Siddhi, J. Verghese, Desai Bhavik. 2017
13 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xu Tan
- Tao Qin
- Tie-Yan Liu
- Frank K. Soong
- Sheng Zhao
- Yi Ren
- Edresson Casanova
- Moacir Antonelli Ponti
In Signal Processing
- Xu Tan
- Tie-Yan Liu
- Tao Qin
- Frank K. Soong
- Moacir Antonelli Ponti
- Edresson Casanova
- Sandra Maria Aluísio
- Frederico Santos de Oliveira
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”
- TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese (2020, background): “In addition, Hidden Markov Model (HMM) based synthesis is still a topic of research [9, 10, 11], as well as a variety of Unit Selection Models [12, 13].”
- End-To-End Speech Synthesis Applied to Brazilian Portuguese (2020, unknown): “ov Model (HMM) based synthesis is still a topic of research [Tokuda et al., 2000, Braude et al., 2013, Aroon and Dhonde, 2015], as well as a variety of Unit Selection Models [Wang and Georgila, 2011, Siddhi et al., 2017]. Deep Learning [Goodfellow et al., 2016] allows to integrate all processing ste”