Back Propagation Neural Network based Emotion Recognition System
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2015 by IJETT Journal|
|Year of Publication : 2015|
|Authors : Rohit Katyal
|DOI : 10.14445/22315381/IJETT-V22P231|
Rohit Katyal"Back Propagation Neural Network based Emotion Recognition System", International Journal of Engineering Trends and Technology (IJETT), V22(4),148-152 April 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Emotion recognition with speech has now a day’s getting attention of engineers in field of pattern recognition and speech signal processing. As computers is one of the important part now a days, so the requirement for communication between computer and humans. Through voice signals Automatic emotion recognition recognise the emotional state of the speaker. In humans life emotions plays a vital role. This paper focuses on the e motion recognition process by including feature extraction of the speech signals and then classification process with BPNN Classifier. In this system, a database of 500 speech signals are used to train and test the system which are of different categories like happy, sad, aggressive and fear. Classification process is depending on different extracted features like Maximum Frequency, Average Frequency, Minimum Frequency, Roll off, Pitch, and Loudness from a speech sample. The classification accuracy achieved by this system varies from 85 to 95 percent.
 B. Jaggi, S. S. Poon, C. MacAulay, and B. Palcic, ``Imaging system for morphometric assessment of absorption or fluorescence in stained cells,`` Cytometry, vol. 9, no. 6, pp. 566-572, 1988.
[2 M. A. Hossain, M. M. Rahman, U. K. Prodhan and M. F. Khan, “Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech Recognition,” International Journal of Information Sciences and Techniques , Vol 3, No.4, pp. 1-9, 2013.
 R. Rojas,” Neural Networks:A Systematic Introduction,” Springer Berlin Heidelberg New York, 2005.
 Ververidis and C. Kotropoulos “Emotional speech recognition: Resources, features,and methods,” Speech Communication , Vol 48, pp. 1162-1181
 J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Advances in Neural Information Processing Systems 11, M. S. Kearns, S. A. Solla, D. A. Cohn, eds., MIT Press, (1999).
 W. Gevaert, G. Tsenov and V. Mladenov, “Neural Networks used for Speech Recognition,” Journal of Automatic Control, Vol. 20, pp. 1-7, 2010.
 M. Cilimkovic, “Neural Networks and Back Propagation Algorithm”, Institute of Technology Blanchardstown, Blanchardstown Road North Dublin 15, Ireland.
 P. Peng, Q. L. Ma and L. Hong, “The Research Of The Parallel Smo Algorithm For Solving Svm,” Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 2009.
 R. Fan, P. Chen and C. Lin, “Working Set Selection Using Second Order Information for Training Support Vector Machines,” Journal of Machine Learning Research Vol. 6 , pp. 1889–1918, 2005.
 X. Shao, KunWu, and B. Liao, “Single Directional SMO Algorithm for Least Squares Support Vector Machines,” Computational Intelligence and Neuroscience Vol., 2013, Article ID 968438.
 F. R. Bach, G. R. G. Lanckriet and M. I. Jordan, “Multiple Kernel Learning, Conic Duality, and the SMO Algorithm”, Proceedings of the 21st International Conference on Machine Learning, 2004.
 D. Rumelhart, G. Hinton, and Williams R., ``Learning representations by back-propagating errors,`` Nature, vol. 323, pp. 533-536, 1986.  Kurt Hornick, Maxwell Stinchcombe, and Halbert White, ``Multilayer feedforward networks are universal approximators,`` Neural Networks, vol. 2, pp. 359-366, 1989.
 Michael D. Richard and Richard P. Lippmann, ``Neural network classifiers estimate bayesian a posteriori probabilities,`` Neural Computation, vol. 3, no. 4, pp. 461-483, 1991.
 E.B. Baum and D. Haussler, ``What size net gives valid generalization?,`` Neural Computation, vol. 1, no. 1, pp. 151-160, 1989.
 N.Pushpa, R.Revathi, C.Ramya and S.Shahul Hameed, “Speech Processing Of Tamil Language with Back Propagation Neural Network and Semi-Supervised Training,” International Journal of Innovative Research in Computer and Communication Engineering, Vol.2 (1), 2014.
 H. Gupta and D. S. Wadhwa, “Speech Feature Extraction and Recognition Using Genetic Algorithm,” International Journal of Emerging Technology and Advanced Engineering, Vol. 4 (1), 2014.
 J. Bachorowski, "Vocal Expression and Perception of Emotion," Current Directions in Psychological Science, Vol. 8 (2), pp. 53–57, 1999.
 D. A. Sauter; F. Eisner, A. J. Calder and S. K. Scott, "Perceptual cues in nonverbal vocal expressions of emotion," The Quarterly Journal of Experimental Psychology, Vol. 63 (11), pp. 2251–2272.
 Y. Pan, P. Shen , and Liping Shen,” Speech Emotion Recognition Using Support Vector Machine” , International Journal of Smart Home Vol. 6, No. 2, April, 2012.
 A. Utane, S.L Nalbalwar, “Emotion Recognition Through Speech Using Gaussian Mixture Model And Hidden Markov Model”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 4, April 2013.
BPNN, Emotion Recognition, Speech Recognition