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Robust continuous digit recognition using reservoir computing

(2015) COMPUTER SPEECH AND LANGUAGE. 30(1). p.135-158
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  • 231267 (ORGANIC)
  • G.0088.09N (RECAP)
Keywords
Automatic Speech Recognition, Recurrent Neural Networks, Noise robust spoken digit recognition, Reservoir Computing, Acoustic modeling, AUTOMATIC SPEECH RECOGNITION, EXTREME LEARNING-MACHINE, VECTOR TAYLOR-SERIES, OPTIMIZATION, UNCERTAINTY, MODELS, NOISE, ADAPTATION, NETWORKS, HYBRID

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Citation

Please use this url to cite or link to this publication:

MLA
Jalalvand, Azarakhsh, Fabian Triefenbach, Kris Demuynck, et al. “Robust Continuous Digit Recognition Using Reservoir Computing.” COMPUTER SPEECH AND LANGUAGE 30.1 (2015): 135–158. Print.
APA
Jalalvand, A., Triefenbach, F., Demuynck, K., & Martens, J.-P. (2015). Robust continuous digit recognition using reservoir computing. COMPUTER SPEECH AND LANGUAGE, 30(1), 135–158.
Chicago author-date
Jalalvand, Azarakhsh, Fabian Triefenbach, Kris Demuynck, and Jean-Pierre Martens. 2015. “Robust Continuous Digit Recognition Using Reservoir Computing.” Computer Speech and Language 30 (1): 135–158.
Chicago author-date (all authors)
Jalalvand, Azarakhsh, Fabian Triefenbach, Kris Demuynck, and Jean-Pierre Martens. 2015. “Robust Continuous Digit Recognition Using Reservoir Computing.” Computer Speech and Language 30 (1): 135–158.
Vancouver
1.
Jalalvand A, Triefenbach F, Demuynck K, Martens J-P. Robust continuous digit recognition using reservoir computing. COMPUTER SPEECH AND LANGUAGE. 2015;30(1):135–58.
IEEE
[1]
A. Jalalvand, F. Triefenbach, K. Demuynck, and J.-P. Martens, “Robust continuous digit recognition using reservoir computing,” COMPUTER SPEECH AND LANGUAGE, vol. 30, no. 1, pp. 135–158, 2015.
@article{5745805,
  author       = {Jalalvand, Azarakhsh and Triefenbach, Fabian and Demuynck, Kris and Martens, Jean-Pierre},
  issn         = {0885-2308},
  journal      = {COMPUTER SPEECH AND LANGUAGE},
  keywords     = {Automatic Speech Recognition,Recurrent Neural Networks,Noise robust spoken digit recognition,Reservoir Computing,Acoustic modeling,AUTOMATIC SPEECH RECOGNITION,EXTREME LEARNING-MACHINE,VECTOR TAYLOR-SERIES,OPTIMIZATION,UNCERTAINTY,MODELS,NOISE,ADAPTATION,NETWORKS,HYBRID},
  language     = {eng},
  number       = {1},
  pages        = {135--158},
  title        = {Robust continuous digit recognition using reservoir computing},
  url          = {http://dx.doi.org/10.1016/j.csl.2014.09.006},
  volume       = {30},
  year         = {2015},
}

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