International Journal of Information Engineering and Electronic Business(IJIEEB)
ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)
Published By: MECS Press
IJIEEB Vol.7, No.4, Jul. 2015
Emotion Recognition System Based On Skew Gaussian Mixture Model and MFCC Coefficients
Full Text (PDF, 575KB), PP.51-57
Emotion recognition is an important research area in speech recognition. The features of the emotions will affect the recognition efficiency of the speech recognition systems. Various techniques are used in identifying the emotions. In this paper a novel methodology for identification of emotions generated from speech signals has been addressed. This system is proposed using Skew Gaussian mixture model. The proposed model has been experimented over a gender independent emotion database. In order to extract the features from the speech signals cepstral coefficients are used. The developed model is tested using real-time speech data set and also using the standard and data set of Berlin. This model is evaluated in the presence of noise and without noise the efficiency of the model is evaluated and is presented by using confusion matrix.
Cite This Paper
M.ChinnaRao, A.V.S.N.Murthy, Ch.Satyanarayana,"Emotion Recognition System Based On Skew Gaussian Mixture Model and MFCC Coefficients", IJIEEB, vol.7, no.4, pp.51-57, 2015. DOI: 10.5815/ijieeb.2015.04.07
Arvid C. Johnson, "Characteristics and Tables of The Left-Truncated Normal Distribution" International Journal of Advanced Computer Science and Applications (IJACSA), pp133-139, May 2001.
Forsyth M. and Jack M., "Discriminating Semi-continuous HMM for Speaker Verification" IEEE Int.conf.Acoust., speech and signal processing, Vol.1, pp313-316, 1994.
Forsyth M., "Discrimination observation probability hmm for speaker verification, speech communication", Vol.17, pp.117-129, 1995.
George A and Constantine K "Phonemic Segmentation Using the Generalized Gamma Distribution and Small Sample Bayesian Information Criterion, speech communication" DOI: 10.1016/j.specom.2007.06.005, June-2007.
Gregor D et al "Emotion Recognition in Borderline Personality Disorder- A review of the literature" journal of personality disorders, 23(1), pp6-9, 2009.
Lin Y.L and Wei G "Speech Emotion Recognition based on HMM and SVM" 4th international conference on machine learning and cybernetics, Guangzhou, Vol.8, pp4898-4901, 18-Aug-2005.
Meena K, Subramanian U, and Muthusamy G "Gender Classification in Speech Recognition using Fuzzy Logic and Neural Network" The International Arab Journal of Information Technology, Vol. 10, No. 5, September 2013, PP477-485.
Prasad A., Prasad Reddy P.V.G.D., Srinivas Y. and Suvarna Kumar G "An Emotion Recognition System based on LIBSVM from telugu rural Dialects of Andhra Pradesh" journal of advanced research in computer engineering: An International journal ,volume 3, Number 2, july-December 2009.
Vibha T "MFCC and its application in speaker recognition" international journal of emerging technology ISSN: 0975-8364 pp19-22.
Kasiprasad Mannepalli, Panyam Narahari Sastryand V. Rajesh "Modelling And Analysis Of Accent Based Recognition And Speaker Identification System" ISSN 1819-6608, Dec 2014 pages 2807-2815.
GSuvarna Kumar et. Al "SPEAKER RECOGNITION USING GMM." International Journal of Engineering Science and Technology, Vol. 2(6), 2010, 2428-2436.
Stavros Ntalampiras and Nikos Fakotakis" Modeling the Temporal Evolution of Acoustic Parameters for Speech Emotion Recognition" IEEE Transactions on affective computing, vol. 3, no. 1, january-march 2012.
K.Sreenivasa Rao Hindi Dialects and Emotions using Spectral and Prosodic features of Speech" Systems, Cybernetics and Informatics Volume 9 - Number 4 .ISSN: 1690-4524.
Chung-Hsien Wu and Wei-Bin Liang "Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels" IEEE Transactions on ffective computing, vol. 2, no. 1, january-march 2011.
Fuzzy-Clustering-Based Decision Tree Approach for Large Population Speaker Identification. Yakun Hu, Dapeng Wu, Fellow, IEEE, and Antonio Nucci. IEEE Transactions on audio, speech, and language processing, vol. 21, no. 4, april 2013.