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Phoneme recognition in timit with blstm-ctc (2008).
| Content Provider | CiteSeerX |
|---|---|
| Author | Fernández, Santiago Graves, Alex Schmidhuber, Jürgen |
| 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. 1 |
| File Format | |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Phoneme Recognition Recurrent Neural Network Timit Database Single Recurrent Neural Network Error Rate Comparable Performance |
| Content Type | Text |
| Resource Type | Article |
