# Text Independent Speaker Identification Model Using Mixture of Pearsonian
Type I Model and K-Means Algorithm

**Title**: Text Independent Speaker Identification Model Using Mixture of Pearsonian
Type I Model and K-Means Algorithm

**Publisher**: Guru Nanak Publications

**ISSN**: 2278-0947

**Series**: Volume 3 Issue 2

**Authors**: K.Srinivasa Rao , P.Chandra Sekhar and M.Sesha sayee

## Abstract

Speaker identification model plays an
important role in authentication and recognition of
persons. Much work has been reported in literature
regarding speaker identification models using
Gaussian mixture model. The Gaussian mixture model
has a prime drawback since it can characterize only
mesokurtic and symmetrically distributed features.
But in many practical situations the feature vector
associated with each speech spectra (Mel-cepstral
coefficients) may not be having a mixture of Gaussian
components. Hence to have accurate the identification
and recognition system, it is needed to develop
generalize text independent speaker identification
system . Hence in this paper we develop and analyses
speaker identification system using mixture of
Pearsonian Type I system of models. The model
parameters are derived in updated equations EM
algorithm. The initialization of model parameters of
EM algorithm is obtained by K-means algorithm and
moment estimates. Using likelihood function under the
baysian frame work, the speaker identification
algorithm is developed. The performance of algorithm
is studied using ROC curves, confusion matrix and
other quality matrices. It is observed that the system
outperforms the model based speaker identification
system models.

## Keywords

Bayesian frame, EM algorithm, Gaussian
mixture model, Likeliood function, Meso kurtic,
Pearsonian type – I system, ROC curves, Speech
spectra, Speaker identification model.

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