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Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling

Fabian Triefenbach UGent, Kris Demuynck UGent and Jean-Pierre Martens UGent (2012) IEEE Workshop on Spoken Language Technology, Proceedings. p.107-112
abstract
In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
reservoir computing, tandem acoustic modeling, continuous speech recognition
in
IEEE Workshop on Spoken Language Technology, Proceedings
pages
107 - 112
publisher
IEEE
place of publication
Piscataway, NJ, USA
conference name
IEEE Workshop on Spoken Language Technology
conference location
Miami, FL, USA
conference start
2012-12-02
conference end
2012-12-05
Web of Science type
Proceedings Paper
Web of Science id
000317182800019
ISBN
9781467351256
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3000500
handle
http://hdl.handle.net/1854/LU-3000500
date created
2012-09-27 16:49:43
date last changed
2013-09-03 15:46:30
@inproceedings{3000500,
  abstract     = {In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20{\textbackslash}\% relative is possible.},
  author       = {Triefenbach, Fabian and Demuynck, Kris and Martens, Jean-Pierre},
  booktitle    = {IEEE Workshop on Spoken Language Technology, Proceedings},
  isbn         = {9781467351256},
  keyword      = {reservoir computing,tandem acoustic modeling,continuous speech recognition},
  language     = {eng},
  location     = {Miami, FL, USA},
  pages        = {107--112},
  publisher    = {IEEE},
  title        = {Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling},
  year         = {2012},
}

Chicago
Triefenbach, Fabian, Kris Demuynck, and Jean-Pierre Martens. 2012. “Improving Large Vocabulary Continuous Speech Recognition by Combining GMM-based and Reservoir-based Acoustic Modeling.” In IEEE Workshop on Spoken Language Technology, Proceedings, 107–112. Piscataway, NJ, USA: IEEE.
APA
Triefenbach, F., Demuynck, K., & Martens, J.-P. (2012). Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling. IEEE Workshop on Spoken Language Technology, Proceedings (pp. 107–112). Presented at the IEEE Workshop on Spoken Language Technology, Piscataway, NJ, USA: IEEE.
Vancouver
1.
Triefenbach F, Demuynck K, Martens J-P. Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling. IEEE Workshop on Spoken Language Technology, Proceedings. Piscataway, NJ, USA: IEEE; 2012. p. 107–12.
MLA
Triefenbach, Fabian, Kris Demuynck, and Jean-Pierre Martens. “Improving Large Vocabulary Continuous Speech Recognition by Combining GMM-based and Reservoir-based Acoustic Modeling.” IEEE Workshop on Spoken Language Technology, Proceedings. Piscataway, NJ, USA: IEEE, 2012. 107–112. Print.