  In this paper, we propose a universal background model, named universal
background sparse coding (UBSC), for speaker verification. The proposed method
trains an ensemble of clusterings by data resampling, and produces sparse codes
from the clusterings by one-nearest-neighbor optimization plus binarization.
The main advantage of UBSC is that it does not suffer from local minima and
does not make Gaussian assumptions on data distributions. We evaluated UBSC on
a clean speech corpus---TIMIT. We used the cosine similarity and inner product
similarity as the scoring methods of a trial. Experimental results show that
UBSC is comparable to Gaussian mixture model.
