Please use this identifier to cite or link to this item:
Title: Application of a fast real time recurrent learning algorithm to text-to-phoneme conversion
Authors: Lu, YL
Mak, MW 
Siu, WC 
Issue Date: 1995
Publisher: IEEE
Source: IEEE International Conference on Neural Networks - Conference Proceedings, 1995, v. 5, p. 2853-2857 How to cite?
Abstract: This paper attempts to perform text-to-phoneme conversion by using recurrent neural networks trained with the real time recurrent learning (RTRL) algorithm. As recurrent neural networks deal well with spatial temporal problems, they are proposed to tackle the problem of converting English text streams into their corresponding phonetic transcriptions. We found that, due to the high computational complexity, the original RTRL algorithm takes a long time to finish the learning. We propose a fast RTRL algorithm (FRTRL), with a lower computational complexity, to shorten the time consumed in the learning process.
Description: Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6), Perth, Aust, 27 November-1 December 1995
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Feb 14, 2019

Page view(s)

Last Week
Last month
Citations as of Feb 11, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.