Text independent speaker identification with finite multivariate generalised Gaussian mixture model and k-means algorithm Online publication date: Sun, 21-Apr-2013
by V. Sailaja; K. Srinivasa Rao; K.V.V.S. Reddy
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 6, No. 2, 2013
Abstract: In this paper, we propose text independent speaker identification with a finite multivariate generalised Gaussian Mixture Model (GMM) with a k-means algorithm. Each speaker's speech spectra are characterised with a mixture of generalised Gaussian distribution that includes Gaussian and Laplacian distribution as a particular case. Speech analysis is done with the Mel Frequency Cepstral Coefficients (MFCC) extracted from the front end process. Using the EM algorithm and k-means algorithm the model parameters the numbers of acoustic classes associated with each speech spectra are determined. The performance of the proposed algorithm is studied through experimental evaluation and observed that this algorithm outperforms the existing speaker identification algorithm with GMM. It is also observed that this algorithm performs efficiently even with a heterogeneous population with small (less than 2 seconds) utterances.
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