Bidirectional LSTM networks for improved phoneme classification and recognition

Alex Graves, Santiago Fernández, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

650 Scopus citations


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 languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages6
StatePublished - Dec 1 2005
Externally publishedYes

Bibliographical note

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Bidirectional LSTM networks for improved phoneme classification and recognition'. Together they form a unique fingerprint.

Cite this