The evaluation of compensation techniques in i-vector modelling for sound-based biometric recognition system

Abstract

i-vector subspace modelling is one of the recent methods that has become attractive to sound-based biometric recognition domain. This method provides a benefit of modelling both intra-domain and inter-domain variability into one low dimensional space. This paper focuses on the analysis of i-vector channel compensation techniques for the purpose of improving the i-vector sound-based biometric recognition performance. This work was mainly motivated by the need to quantify the impact of different compensation techniques to the i-vector performance specifically towards the fusion compensation approach. The performances of six channel compensation techniques: (a) whitening, (b) Within Class Covariance Normalization (WCCN), (c) Linear Discriminant Analysis (LDA), (d) whitening and WCCN, (e) whitening and LDA and (f) WCCN and LDA have been investigated in this study. 2656 syllables of bio-acoustic sounds are used as experimental data and parameters of the system are initially tuned with different GMM component sizes i.e. 16, 32, 64 and 128 number of Gaussians. To the end, we assess the effect of the tuned parameter and observe the recognition rate. Experimental results reveal that the accuracy of i-vector with the fusion of WCCN and LDA compensation outperforms other compensation approaches with result of 92.00%. Consequently, these findings allow a better understanding of the compensation approaches, in particular, the fundamental concept of the compensation procedure that leads to the success of the i-vector paradigm.