Evaluation of Speaker Identification System using GSMEFR speech Data

Abstract

This paper investigate the influence of GSMEFR speech Data on the performance of a text independent Speaker Identification System (SIS) based on Gaussian Mixture Models (GMM) classifiers. The performance evaluation due to the use of the GSMEFR speech Data, obtained by passing the local ARADIGIT database through the GSM coder/decoder. The recognition evaluation was also conducted using original ARADIGIT sampled at 16 KHz and its 8 KHz downsampled version. The ARADIGIT database consists of 60 speakers (31 men and 29 women) pronouncing the ten Arabic digits three times each. Different experiments were carried to measure the degradation introduced by different aspects of the simulated codec.