In Human Computer Interaction (HCI) area speech emotion recognition is one of the popular topic in the world. Many researchers are engaged in developing systems to recognize different emotions from human speech. This is done to make HCI and human interface more effective and develop systems like humans. In this paper we have stated the basics of speech emotion recognition system and reviewed different feature extraction and classification technique for the system. Features are classified as Elicited features, Prosodic features and Spectral features. Different classifying techniques are used to classify different emotions from human speech like Hidden Markov Model (HMM), Gaussian Mixtures Model (GMM), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-nearest neighbor (KNN). Performance of classifiers are also discussed shortly. Different applications where speech emotion recognition systems are used are also discussed in last section of the paper.

Emotion, Speech, Emotional Speech database, Elicited featues, HMM, GMM, SVM, ANN, KNN, Application

[1] V. B. Waghmare, R. R. Deshmukh, P. P. Shrishrimal, G. B. Janvale,"Emotion Recognition System from Artificial Marathi Speech using MFCC and LDA Techniques ," Proc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC, Elsevier, 2014.
[2] R. A. S. Nilofer, R. P. Gadhe, R. Deshmukh, V. B. Wasghmare, P. P. Shrishrimal, "Automatic emotion recognition from speech signals: A review", International Journal of Scientific and Engineering Research, vol. 6, no. 4, April 2015.
[3] S. Emerich, E. Lupu, A. Apatean, “Emotions Recognitions by Speech and Facial Expressions Analysis”, 17th European Signal Processing Conference, 2009.
[4] A. Mohanta and U. Sharma, "Bengali speech emotion recognition," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 2812-2814 (IEEE).
[5] D. D. Joshi, M. B. Zalte, "Speech Emotion Recognition: A Review", IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), pp. 34-37, January-February 2013.
[6] A Mohanta, U Sharma, "Human Emotion Recognition through Speech", Advances in Computer Scienceand Information Technology (ACSIT), vol. 2, no. 10, pp. 29-32, April-June 2015.
[7] A. S. Utane, S. L. Nalbalwar, "Emotion Recognition through Speech", International Journal of Applied Information Syatems (IJAIS), pp. 5-8, 2013.
[8] Ayadi, M. E., Kamel, M. S., & Karray, F. “Survey on speech emotion recognition: features, classification schemes, and databases”. Pattern Recognition, 44, 572–587 2011..
[9] Idris I., Salam M.S. “Improved Speech Emotion Classification from Spectral Coefficient Optimization”.Lecture Notes in Electrical Engineering, vol 387. Springer, 2016.
[10] Pao TL., Chen YT., Yeh JH., Cheng YM., Chien C.S. “Feature Combination for Better Differentiating Anger from Neutral in Mandarin Emotional Speech”, LNCS: Vol. 4738 Berlin:Springer 2007.
[11] Yashpalsing Chavhan, M. L. Dhore, Pallavi Yesaware, "Speech Emotion Recognition Using Support Vector Machine", International Journal of Computer Applications, vol. 1, pp.6-9,February 2010.
[12] A.P. Wanare, S.N. Dandare, "Human Emotion Recognition From Speech", Int. Journal of Engineering Research and Applications, vol. 4, no. 7, pp. 74-78, July 2014.
[13] A. Joshi, "Speech Emotion Recognition Using Combined Features of HMM & SVM Algorithm," International Journal of Advanced Research in Computer Science and Software Engineering, pp. 387-392, 2013.
[14] A. B Ingale and D. S. Chaudhari, “Speech Emotion Recognition”, International Journal of Soft Computing and Engineering (IJSCE), March 2012, pp. 235-238.
[15] A. S. Utane and S.L. Nalbalwar, “Emotion Recognition through Speech”, International Journal of Applied Information Systems (IJAIS), 2013, pp. 5-8.