Rohan K. Das

Work place: Indian Institute of Technology Guwahati, Guwahati-781039, India



Research Interests: Speech Recognition, Pattern Recognition, Computational Learning Theory


Rohan Kumar Das received B. Tech degree in Electronics and Communication Engineering from North-Eastern Hill University (NEHU), Shillong, India in the year 2010 and PhD degree in Electronics and Electrical Engineering from Indian Institute of Technology (IIT) Guwahati, Assam in the year 2017. Currently he is pursuing his post-doctoral studies in National University of Singapore (NUS), Singapore. He has also worked in Kovid Research Labs. His research interests are speech signal processing, speaker verification, machine learning and pattern recognition.

Author Articles
Significance of Source Information for Text Dependent Speaker Verification

By Archita Hore S. R. Nirmala Rohan K. Das Sarfaraz Jelil S. R. M. Prasanna

DOI:, Pub. Date: 8 Jun. 2018

This work focuses on text dependent speaker verification system where a source feature specifically residual Mel frequency cepstral coefficients (RMFCC), has been extracted in addition to a vocal tract system feature namely Mel frequency cepstral coefficients (MFCC). The RMFCC features are derived from the LP residuals whereas MFCC features are derived from the cepstral analysis of the speech signal. Thus, these two features have different information about the speaker. A four cohort speaker’s set has been prepared using these two features and dynamic time warping (DTW) is used as the classifier. Performance comparison of the text dependent speaker verification model using MFCC and RMFCC features are enumerated. Experimental results shows that, using RMFCC feature alone do not give satisfactory results in comparison to MFCC. Also, the system’s performance obtained using the MFCC features, is not optimum. So, to improve the performance of the system, these two features are combined together using different combination algorithms. The proposed lowest ranking method yields good performance with an equal error rate (EER) of 7.50%. To further improve the efficiency of the system, the proposed method is combined along with the strength voting and weighted ranking method in the hierarchical combination method to obtain an EER of 3.75%. 

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