Abstract
This paper presents the fusion of artificial intelligence (AI) learning algorithms that combined genetic algorithms (GA) and neural network (NN) methods. These both methods were used to find the optimum weights for the hidden and output layers of feed-forward artificial neural network (ANN) model. Both algorithms are the separate modules and we proposed dynamic connection strategy for combining both algorithms to improve the recognition performance for isolated spoken Malay speech recognition. There are two different GA techniques used in this research, one is standard GA and slightly different technique from standard GA also has been proposed. Thus, from the results, it was observed that the performance of proposed GA algorithm while combined with NN shows better than standard GA and NN models alone. Integrating the GA with feed-forward network can improve mean square error (MSE) performance and with good connection strategy by this two stage training scheme, the recognition rate can be increased up to 99%.
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References
Deller, J.R., Proakis, J.G., Hansen, J.H.L.: Discrete-Time Processing of Speech Signal. Macmillan, New York (1993)
Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustic, Speech and Signal Processing 1975 23(1), 67–72 (1975)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustic, Speech and Signal Processing 26(1), 43–49 (1978)
Panayiota, P., Costa, N., Costantinos, S.P.: Classification capacity of a modular neural network implementing neurally inspired architecture and training rules. IEEE Transactions on Neural Networks 15(3), 597–612 (2004)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In: Parallel Distributed Processing, Exploring the Macro Structure of Cognition. MIT Press, Cambridge (1986)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2001)
Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Britannica, Encyclopedia Britannica Online (2007), http://www.britannica.com/eb/article-9050292
Hornik, K.J., Stinchcombe, D., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2(5), 359–366 (1989)
Ghosh, R., Yearwood, J., Ghosh, M., Bagirov, A.: Hybridization of neural learning algorithms using evolutionary and discrete gradient approaches. Computer Sciernce Journal 1(3), 387–394 (2005)
Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. University of Colorado, Campus Publishing Service (1996)
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© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Seman, N., Bakar, Z.A., Bakar, N.A. (2014). Dynamic Neuro-genetic Weights Connection Strategy for Isolated Spoken Malay Speech Recognition System. In: Das, V.V., Elkafrawy, P. (eds) Signal Processing and Information Technology. SPIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-11629-7_18
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DOI: https://doi.org/10.1007/978-3-319-11629-7_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11628-0
Online ISBN: 978-3-319-11629-7
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