Abstract

This paper presents a simplified and supervised i-vector modeling framework that is applied in the task of robust and efficient speaker verification (SRE). First, by concatenating the mean supervector and the i-vector factor loading matrix with respectively the label vector and the linear classifier matrix, the traditional i-vectors are then extended to label-regularized supervised i-vectors. These supervised i-vectors are optimized to not only reconstruct the mean supervectors well but also minimize the mean squared error between the original and the reconstructed label vectors, such that they become more discriminative. Second, factor analysis (FA) can be performed on the pre-normalized centered GMM first order statistics supervector to ensure that the Gaussian statistics sub-vector of each Gaussian component is treated equally in the FA, which reduces the computational cost significantly. Experimental results are …

Date

2013

Authors

Ming Li, Andreas Tsiartas, Maarten Van Segbroeck, Shrikanth S Narayanan

Conference

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Pages

7199-7203

Publisher

IEEE