Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model and Hierarchical Clustering Algorithm

Abstract

In this paper we propose a Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Hierarchical Clustering. Each speaker speech spectra are characterized with a mixture of Generalized Gaussian Distribution includes Gaussian and Laplacian distribution as a particular case. It also includes several of the platy, lepto and meso kurtic shapes of the speech spectra. The speech analysis is done with Mel Frequency Cepstral Coefficients extracted from front end process. Using the EM algorithm the model parameters are estimated. The numbers of acoustic classes associated with each speech spectra are determined through Hierarchical clustering. The performance of the proposed algorithm is studied through experimental evolution with 100 speaker"s data base and found that this algorithm outperforms the existing speaker identification algorithm with GMM. It is also observed that this algorithm performs efficiently even heterogeneous population with small (less than 2 seconds utterances)

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

“…Sailaja et al [92] proposed clustering technique a Text Independent Speaker Identification with Finite Generalized Gaussian Mixture Model, which is also Multivariate with Hierarchical Clustering and used the EM algorithm for estimating the parameters. In addition, through Hierarchical clustering, the numbers of acoustic classes associated with each speech spectra are determined.…”

Text Independent Speaker Identification Using Imfcc Integrated With Ica

“…To find the estimate of model parameters αi μij and ij for i= 1,2,3 …,M, j=1,2,…,D, we maximize the expected value b y using log likelihood function.the parameters are given by Armando.j el at (2003) [14] for each speech spectra The following are the updated equations of the parameters of EM algorithm as given by sailaja et al (2010) [15] ( + ) = …”

The Assumption of Non-Gaussianity in Natural and Social Sciences and Its Influence on Detection of Causal Relationships

“…Mixtures of generalized Gaussian distributions have been recently used in text independent speaker identification (Sailaja et al, 2010) and showed that it outperforms the earlier existing text independent speaker identification models. This model was applied for speaker identification like voice dialing, banking by telephone, telephone shopping information services etc.…”