Application of a fast real time recurrent learning algorithm to text-to-phoneme conversion

Yee Ling Lu, Man Wai Mak, Wan Chi Siu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Number of pages5
Publication statusPublished - 1 Dec 1995
EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Australia
Duration: 27 Nov 19951 Dec 1995


ConferenceProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)

ASJC Scopus subject areas

  • Software

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