Phoneme recognition in TIMIT with BLSTM-CTC

04/21/2008

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by   Santiago Fernández, et al.

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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 is not significantly different from that obtained by the other best methods, but they rely on a combination of classifiers for achieving comparable performance.