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Large vocabulary continuous speech recognition with reservoir-based acoustic models

Fabian Triefenbach, Kris Demuynck UGent and Jean-Pierre Martens UGent (2014) IEEE SIGNAL PROCESSING LETTERS. 21(3). p.311-315
abstract
Thanks to recent research in neural network based acoustic modeling, Large Vocabulary Continuous Speech Recognition (LVCSR) has very recently made a significant step forwards. In search for further progress, the present paper investigates Reservoir Computing (RC) as an alternative new paradigm for acoustic modeling. This paradigm unifies the appealing dynamical modeling capacity of a recurrent neural network (RNN) with the simplicity and robustness of linear regression as a model for training the weights of that network. In previous work, an RC-HMM hybrid yielding good phone recognition accuracy on TIMIT could be designed, but no proof was offered that this success would also transfer to LVCSR. This paper describes the development of an RC-HMM hybrid that offers good recognition on the Wall Street Journal benchmark. For the 5K word task, word error rates on the Nov92 evaluation set are as low as 6.2% (bigram language model) and 3.9% (trigram). Given that RC-based acoustic modeling is a fairly new approach, these results open up very promising perspectives.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (letterNote)
publication status
published
subject
keyword
Acoustic Modeling, Recurrent Neural Networks, Reservoir Computing, Large Vocabulary Continuous Speech Recognition, SHORT-TERM-MEMORY, NETS
journal title
IEEE SIGNAL PROCESSING LETTERS
IEEE SPL
volume
21
issue
3
pages
311 - 315
Web of Science type
Article
Web of Science id
000331810800003
JCR category
ENGINEERING, ELECTRICAL & ELECTRONIC
JCR impact factor
1.751 (2014)
JCR rank
81/249 (2014)
JCR quartile
2 (2014)
ISSN
1070-9908
DOI
10.1109/LSP.2014.2302080
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
4234359
handle
http://hdl.handle.net/1854/LU-4234359
date created
2014-01-16 12:43:13
date last changed
2016-12-19 15:45:38
@article{4234359,
  abstract     = {Thanks to recent research in neural network based acoustic modeling, Large Vocabulary Continuous Speech Recognition (LVCSR) has very recently made a significant step forwards. In search for further progress, the present paper investigates Reservoir Computing (RC) as an alternative new paradigm for acoustic modeling. This paradigm unifies the appealing dynamical modeling capacity of a recurrent neural network (RNN) with the simplicity and robustness of linear regression as a model for training the weights of that network. In previous work, an RC-HMM hybrid yielding good phone recognition accuracy on TIMIT could be designed, but no proof was offered that this success would also transfer to LVCSR. This paper describes the development of an RC-HMM hybrid that offers good recognition on the Wall Street Journal benchmark. For the 5K word task, word error rates on the Nov92 evaluation set are as low as 6.2\% (bigram language model) and 3.9\% (trigram). Given that RC-based acoustic modeling is a fairly new approach, these results open up very promising perspectives.},
  author       = {Triefenbach, Fabian and Demuynck, Kris and Martens, Jean-Pierre},
  issn         = {1070-9908},
  journal      = {IEEE SIGNAL PROCESSING LETTERS},
  keyword      = {Acoustic Modeling,Recurrent Neural Networks,Reservoir Computing,Large Vocabulary Continuous Speech Recognition,SHORT-TERM-MEMORY,NETS},
  language     = {eng},
  number       = {3},
  pages        = {311--315},
  title        = {Large vocabulary continuous speech recognition with reservoir-based acoustic models},
  url          = {http://dx.doi.org/10.1109/LSP.2014.2302080},
  volume       = {21},
  year         = {2014},
}

Chicago
Triefenbach, Fabian, Kris Demuynck, and Jean-Pierre Martens. 2014. “Large Vocabulary Continuous Speech Recognition with Reservoir-based Acoustic Models.” Ieee Signal Processing Letters 21 (3): 311–315.
APA
Triefenbach, F., Demuynck, K., & Martens, J.-P. (2014). Large vocabulary continuous speech recognition with reservoir-based acoustic models. IEEE SIGNAL PROCESSING LETTERS, 21(3), 311–315.
Vancouver
1.
Triefenbach F, Demuynck K, Martens J-P. Large vocabulary continuous speech recognition with reservoir-based acoustic models. IEEE SIGNAL PROCESSING LETTERS. 2014;21(3):311–5.
MLA
Triefenbach, Fabian, Kris Demuynck, and Jean-Pierre Martens. “Large Vocabulary Continuous Speech Recognition with Reservoir-based Acoustic Models.” IEEE SIGNAL PROCESSING LETTERS 21.3 (2014): 311–315. Print.