Data-driven tree structure based UBM reconstruction for speaker verification

In this paper, a universal background model (UBM) reconstruction approach is presented, by means of modeling the acoustic space hierarchically and then associating each leaf node of the tree with the most relevant Gaussians. This data-driven algorithm is investigated to potentially utilize broad phonetic-specific speaker characteristics by Gaussian mixture model (GMM). We introduce cumulative posterior probability based Gaussian selection and distance measure based UBM parameter estimation using the tree structure. Analysis and validation of the UBM generated by Gaussian tying are provided in this paper. Performance evaluations are conducted on GMM-supervector and I-vector speaker verification systems, respectively. The experimental results on the NIST SRE 2006 show that the performance can be improved consistently compared to the baseline system.