On Improving the Classification Capability of Reservoir Computing for Arabic Speech Recognition
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.
KeywordsReservoir computing Speech recognition PLP MFCC RASTA-PLP Speech corpus Arabic language
Unable to display preview. Download preview PDF.
- 1.Jaeger, H.: The ”echo state” approach to analysing and training recurrent neural networks-with an erratum note. Tecnical report GMD report 148 (2001)Google Scholar
- 3.Verstraeten, D.: Reservoir computing: computation with dynamical systems. Electronics and Information Systems, Gent. Ghent University (2009)Google Scholar
- 4.Lukoševičius, M., Jaeger, H., Schrauwen, B.: Reservoir computing trends. KI-Künstliche Intelligenz, 1–7 (2012)Google Scholar
- 5.Lukoševičius, M.: A practical guide to applying echo state networks. Neural Networks: Tricks of the Trade, 659–686 (2012)Google Scholar
- 6.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)Google Scholar
- 9.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)Google Scholar
- 10.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)Google Scholar
- 11.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)Google Scholar
- 12.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)Google Scholar