Bidirectional LSTM networks for improved phoneme classification and recognition

Alex Graves; Santiago Fernández; Jürgen Schmidhuber

Bidirectional LSTM networks for improved phoneme classification and recognition

Alex Graves, Santiago Fernández, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. © Springer-Verlag Berlin Heidelberg 2005.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	799-804
Number of pages	6
State	Published - Dec 1 2005
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Cite this

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title = "Bidirectional LSTM networks for improved phoneme classification and recognition",

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. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.",

author = "Alex Graves and Santiago Fern{\'a}ndez and J{\"u}rgen Schmidhuber",

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Bidirectional LSTM networks for improved phoneme classification and recognition. / Graves, Alex; Fernández, Santiago; Schmidhuber, Jürgen.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 799-804.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Fernández, Santiago

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PY - 2005/12/1

Y1 - 2005/12/1

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AB - 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. © Springer-Verlag Berlin Heidelberg 2005.

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M3 - Conference contribution

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EP - 804

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Bidirectional LSTM networks for improved phoneme classification and recognition

Abstract

Bibliographical note

ASJC Scopus subject areas

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