Framewise phoneme classification with bidirectional LSTM and other neural network architectures

  title={Framewise phoneme classification with bidirectional LSTM and other neural network architectures},
  author={Alex Graves and J{\"u}rgen Schmidhuber},
  journal={Neural networks : the official journal of the International Neural Network Society},
  volume={18 5-6},
In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a modified, full gradient version of the LSTM learning algorithm. We evaluate Bidirectional LSTM (BLSTM) and several other network architectures on the benchmark task of framewise phoneme classification, using the TIMIT database. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short Term Memory (LSTM) is much faster and also more accurate than both standard Recurrent… CONTINUE READING
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1999).On supervised learning from sequential data with applications for speech recognition

  • M. Schuster
  • 1999
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11 Excerpts

A new approach to continuous speech recognition using LSTM recurrent neural networks

  • D. Eck, A. Graves, J. Schmidhuber
  • Technical Report IDSIA-1403, IDSIA,…
  • 2003
1 Excerpt

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