On the Use of MFCC Feature Vector Clustering for Efficient Text Dependent Speaker Recognition

The paper describes an experimental study and the development of a computer agent for Speaker recognition. It presents an efficient method to verify authorised speakers and identify them using MFCC Feature vector clustering. For clustering of the MFCC features, Vector Quantisation using Linde-Buzo-Gray (LBG) algorithm has been presented. This approach proves to be an efficient ASR technique.

[1]  H. B. Kekre,et al.  Speaker recognition using Vector Quantization by MFCC and KMCG clustering algorithm , 2012, 2012 International Conference on Communication, Information & Computing Technology (ICCICT).

[2]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[3]  Douglas A. Reynolds,et al.  An overview of automatic speaker recognition technology , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Alex Acero,et al.  Spoken Language Processing: A Guide to Theory, Algorithm and System Development , 2001 .

[5]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[6]  Ronald W. Schafer,et al.  Digital Processing of Speech Signals , 1978 .

[7]  Chiyomi Miyajima,et al.  Speaker identification using Gaussian mixture models based on multi-space probability distribution , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).