Phoneme recognition in TIMIT with BLSTM-CTC
Computation and Language
2008-04-22 v1 Neural and Evolutionary Computing
Abstract
We compare the performance of a recurrent neural network with the best results published so far on phoneme recognition in the TIMIT database. These published results have been obtained with a combination of classifiers. However, in this paper we apply a single recurrent neural network to the same task. Our recurrent neural network attains an error rate of 24.6%. This result is not significantly different from that obtained by the other best methods, but they rely on a combination of classifiers for achieving comparable performance.
Cite
@article{arxiv.0804.3269,
title = {Phoneme recognition in TIMIT with BLSTM-CTC},
author = {Santiago Fernández and Alex Graves and Juergen Schmidhuber},
journal= {arXiv preprint arXiv:0804.3269},
year = {2008}
}
Comments
8 pages