Application of a fast real time recurrent learning algorithm to text-to-phoneme conversion
Yee-Ling Lu, Man‐Wai Mak, Wan-Chi Siu · 2002
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.