Abstract
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.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Graves, A., Fernández, S., Schmidhuber, J. (2005). Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_126
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DOI: https://doi.org/10.1007/11550907_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
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