In this paper, we apply bidirectional training to a Long Short Term Memory (LSTM) network for the first time. We also present a modified, full gradient version of the LSTM learning algorithm. We discuss the significance of framewise phoneme classification to continuous speech recognition, and the validity of using bidirectional networks for online causal tasks. On the TIMIT speech database, we measure the framewise phoneme classification scores of bidirectional and unidirectional variants of both LSTM and conventional Recurrent Neural Networks (RNNs). We find that bidirectional LSTM outperforms both RNNs and unidirectional LSTM. © 2005 IEEE.
|Title of host publication
|Proceedings of the International Joint Conference on Neural Networks
|Number of pages
|Published - Dec 1 2005