Work place: JNTU, Kakinada-533003, India

E-mail: chinnarao.mortha@gmail.com


Research Interests: Pattern Recognition, Image Compression, Image Manipulation, Image Processing, Speech Recognition, Speech Synthesis, Data Mining, Data Structures and Algorithms


Mortha.ChinnaRao received the B.Tech degree in Computer Science and Engineering from JNTU, Hyderabad, Andhra Pradesh, India, in 2004. And also received M.Tech, in Software Engineering from JNTU, Hyderabad, Andhra Pradesh, India, in 2008. Presently He is pursuing PhD in JNTUK, Kakinada, and Andhra Pradesh, India. He is working as Associate Professor in KIET, Kakinada. His research interests including Data mining and Emotion recognition and speech recognition in Image processing.He is member of Computer society of India and Indian Society for Technical Education.
Dr. Akella. V. S. N. Murthy is working as Professor in Mathematics at Aditya Engineering College, Adityanagar, Surampalem, Near Kakinada, and Andhra Pradesh, India. He received PhD from Andhra University in March 2007. He has 17 years of Teaching experience and more than 10 years of Research experience. He published Research papers in reputed National and International Journals.

Author Articles
Emotion Recognition System Based On Skew Gaussian Mixture Model and MFCC Coefficients

By M.ChinnaRao A.V.S.N. Murty Ch.Satyanarayana

DOI: https://doi.org/10.5815/ijieeb.2015.04.07, Pub. Date: 8 Jul. 2015

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.

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