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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition
verfasst von : Alex Graves, Santiago Fernández, Jürgen Schmidhuber
Erschienen in: Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005
Verlag: Springer Berlin Heidelberg
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In this paper, we carry out two experiments on the TIMIT speech corpus with bidirectional and unidirectional Long Short Term Memory (LSTM) networks. In the first experiment (framewise phoneme classification) we find that bidirectional LSTM outperforms both unidirectional LSTM and conventional Recurrent Neural Networks (RNNs). In the second (phoneme recognition) we find that a hybrid BLSTM-HMM system improves on an equivalent traditional HMM system, as well as unidirectional LSTM-HMM.