Investigation of distance effect on gaussian mixture models in speaker identification

Abstract EN

This paper investigates the effect of distance on the Gaussian Mixture Models (GMM) for text dependent speaker identification.

Three stages are used for three different distances from the microphone (1m, 2m, and 3m).

The set of feature extraction used here include Mel frequency cepstral coefficient (MFCC), Bark frequency cepstral coefficient (BFCC) and linear predictive cepstral coefficient (LPCC).

These features are obtained from 20 speakers (10 adults and 10 children) ; all spoke five Arabic words in 5 seconds.

The set of classification includes two types GMM and multilayer perceptron neural network (MLP).

Total results show that MFCC has the best performance in feature extraction, and GMM has better recognition than MLP as total recognition in GMM is 93.15 % and recognition in MLP is 88.06 %.

The results show also that the recognition rate decreases from 93.15 % to 80.82 % as the distance is increased from 1m to 3m.