Abstract
Designing noise-resilient systems is a major challenge in the field of automated speech recognition (ASR). These systems are crucial for real-world applications where high levels of noise tend to be present. We introduce a noise robust system based on Echo State Networks and Extreme Kernel machines which we call ESNEKM. To evaluate the performance of the proposed system, we used our recently released public Arabic speech dataset and the well-known spoken Arabic digits (SAD) dataset. Different feature extraction methods considered in this study include mel-frequency cepstral coefficients (MFCCs), perceptual linear prediction (PLP) and RASTA- perceptual linear prediction. These extracted features were fed to the ESNEKM and the result compared with a baseline hidden Markov model (HMM), so that nine models were compared in total. ESNEKM models outperformed HMM models under all the feature extraction methods, noise levels, and noise types. The best performance was obtained by the model that combined RASTA-PLP with ESNEKM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jaeger, H.: The ”echo state” approach to analysing and training recurrent neural networks-with an erratum note. Tecnical report GMD report 148 (2001)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42, 513–529 (2012)
Verstraeten, D.: Reservoir computing: computation with dynamical systems. Electronics and Information Systems, Gent. Ghent University (2009)
Lukoševičius, M., Jaeger, H., Schrauwen, B.: Reservoir computing trends. KI-Künstliche Intelligenz, 1–7 (2012)
Lukoševičius, M.: A practical guide to applying echo state networks. Neural Networks: Tricks of the Trade, 659–686 (2012)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: A new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990. IEEE (2004)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2, 107–122 (2011)
Triefenbach, F., Martens, J.P.: Can non-linear readout nodes enhance the performance of reservoir-based speech recognizers? In: 2011 First International Conference on Informatics and Computational Intelligence (ICI), pp. 262–267 (2011)
Alalshekmubarak, A., Smith, L.S.: A novel approach combining recurrent neural network and support vector machines for time series classification. In: 2013 9th International Conference on Innovations in Information Technology (IIT), pp. 42–47 (2013)
Hammami, N., Bedda, M.: Improved tree model for arabic speech recognition. In: 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 5, pp. 521–526 (2010)
Hammami, N., Bedda, M., Nadir, F.: The second-order derivatives of mfcc for improving spoken arabic digits recognition using tree distributions approximation model and hmms. In: 2012 International Conference on Communications and Information Technology (ICCIT), pp. 1–5 (2012)
Cavalin, P.R., Sabourin, R., Suen, C.Y.: Logid: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of hmms. Pattern Recognition 45, 3544–3556 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Alalshekmubarak, A., Smith, L.S. (2014). On Improving the Classification Capability of Reservoir Computing for Arabic Speech Recognition. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-11179-7_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
eBook Packages: Computer ScienceComputer Science (R0)