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Speech Recognition using the Epochwise Back Propagation through Time Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 95 - Number 21 |
| Year of Publication: 2014 |
| Authors: Neelima Rajput, S. K. Verma |
10.5120/16718-7036
|
Neelima Rajput, S. K. Verma . Speech Recognition using the Epochwise Back Propagation through Time Algorithm. International Journal of Computer Applications. 95, 21 ( June 2014), 17-21. DOI=10.5120/16718-7036
Abstract
In this paper, the artificial neural networks are implemented to accomplish the English alphabet speech recognition. The design an accurate and effective speech recognition system is a challenging task in the area of speech recognition. We implemented a new data classification method, where we use neural networks, which are trained and performance can be defined on the basis of recognition rate. This method gave comparable result to the already implemented neural networks. In this paper, Back propagation neural network architecture used to recognize the time varying input data, and provides better accurate results for the English Alphabet speech recognition. The Epochwise Back Propagation through time (BPTT) algorithm uses the epoch values of input signal to train the network structures and yields the satisfactory results.
References
- Jianliang Meng, Junwei Zhang,Haoquan Zhao, "Overview of the speech Recognition Technology", 2012 Fourth International Conference on Computational and Information Sciences.
- L. Fausset, Fundamentals of Neural Networks. PrenticeHall Inc. , 1994, ch 4.
- Jiang Ming Hu, in the Yuan Baozong, Lin Biqin. Neural networks for speech recognition research and progress. Telecommunications Science, 1997, 13(7):1-6.
- H. Boulard and N. Morgan, "Continuous speech recognition by connectionist statistical methods," IEEE Trans. Neural Netw. , vol. 4, no. 6, pp. 893–909, Nov. 1993.
- Mohamad Adnan Al-Alaoui, Lina Al-Kanj, Jimmy Azar, and Elias Yaacoub, "Speech recognition using Artificial Neural Network and Hidden Markov Model", IEEE Multidisciplinary Engineering Education Magazine Vol. 3, No. 3, September 2008.
- RICHARD P. LIPPMANN, "Neural Network Classifiers for Speech Recognition" The Lincoln Laboratory Journal, Volume 1, Number 1 (1988)
- Gulin Dede, Murat Husnu Sazl?, "Speech recognition using artificial neural network. " Digital signal processing © 2009 Elsevier Inc.
- George E. Dahl, Dong Yu, Li Deng, Alex Acero," Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition" , IEEE Transaction on Audio, and Language Processing, Vol. 20, No. 1, January 2012.
- H. Paugam-Moisy, 'Parallel neural computing based on network duplicating',in Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks, ed. , I. Pitas, 305–340, JohnWiley, (1993).
- Stefano Scanzio, Sandro Cumani, Roberto Gemello, Franco Mana, P. Laface, "Parallel implementation of Artificial Neural Network Training for Speech Recognition. " Pattern recogonition letters, © 2010 Elsevier B. V.
- N. Morgan and H. Bourlard, "Continuous speech recognition using multilayer perceptrons with hidden Markov models," in Proc. ICASSP,1990, pp. 413–416.
- Y. Hifny and S. Renals, "Speech recognition using augmented conditional random fields," IEEE Trans. Audio, Speech, Lang. Process. , vol. 17, no. 2, pp. 354–365, Feb. 2009.
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