Please use this identifier to cite or link to this item:
https://elib.bsu.by/handle/123456789/233379| Title: | Overview of speech synthesis using LSTM neural networks |
| Authors: | Navickas, G. Korvel, G. Bernataviciene, J. |
| Keywords: | ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика |
| Issue Date: | 2019 |
| Publisher: | Minsk : BSU |
| Citation: | Computer Data Analysis and Modeling: Stochastics and Data Science : Proc. of the Twelfth Intern. Conf., Minsk, Sept. 18-22, 2019. – Minsk : BSU, 2019. – P. 257-261. |
| Abstract: | Currently, the most popular speech recognition systems are based on unit selection - decision tree algorithms. In literature, new speech synthesis methods based on Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) are proposed. In this paper, an overview of speech synthesis and their realization called LSTM is given. Directions for further investigations are high-lighted |
| URI: | http://elib.bsu.by/handle/123456789/233379 |
| ISBN: | 978-985-566-811-5 |
| Appears in Collections: | 2019. Computer Data Analysis and Modeling : Stochastics and Data Science |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 257-261.pdf | 371,56 kB | Adobe PDF | View/Open |
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