Nagaraj V. Dharwadkar

Work place: Department of Computer science and Engineering, Rajarambapu Institute of Technology, Islampur 415414, India



Research Interests: Image Compression, Image Manipulation, Network Security, Image Processing, Data Structures and Algorithms


Nagaraj V. Dharwadkar obtained B.E. in Computer Science and Engineering in 2000 from Karnataka University Dharwad, M.Tech. in Computer Science and Engineering in the year 2006 from VTU, Belgum and Ph.D. in Computer Science and Engineering in 2014 from National Institute of Technology, Warangal. He is Professor and Head of the Computer Science and Engineering department at Rajarambapu Institute of Technology, Islampur. He had 15 years of Teaching Experience at Professional Institutes across India and published 40 papers in various International Journals and Conferences. His area of research interest is Multimedia Security, Image Processing, Big Data Analytics and Machine Learning.

Author Articles
A Speaker Recognition System Using Gaussian Mixture Model, EM Algorithm and K-Means Clustering

By Ajinkya N. Jadhav Nagaraj V. Dharwadkar

DOI:, Pub. Date: 8 Nov. 2018

The automated speaker endorsement technique used for recognition of a person by his voice data. The speaker identification is one of the biometric recognition and they were also used in government services, banking services, building security and intelligence services like this applications. The exactness of this system is based on the pre-processing techniques used to select features produced by the voice and to identify the speaker, the speech modeling methods, as well as classifiers, are used. Here, the edges and continuous quality point are eliminated in the normalization process. The Mel-Scale Frequency Cepstral Coefficient is one of the methods to grab features from a wave file of spoken sentences. The Gaussian Mixture Model technique is used and done experiments on MARF (Modular Audio Recognition Framework) framework to increase outcome estimation. We have presented an end pointing elimination in Gaussian selection medium for MFCC.

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