  This paper presents a linear regression based back-end for speaker
verification. Linear regression is a simple linear model that minimizes the
mean squared estimation error between the target and its estimate with a closed
form solution, where the target is defined as the ground-truth indicator
vectors of utterances. We use the linear regression model to learn speaker
models from a front-end, and verify the similarity of two speaker models by a
cosine similarity scoring classifier. To evaluate the effectiveness of the
linear regression model, we construct three speaker verification systems that
use the Gaussian mixture model and identity-vector (GMM/i-vector) front-end,
deep neural network and i-vector (DNN/i-vector) front-end, and deep vector
(d-vector) front-end as their front-ends, respectively. Our empirical
comparison results on the NIST speaker recognition evaluation data sets show
that the proposed method outperforms within-class covariance normalization,
linear discriminant analysis, and probabilistic linear discriminant analysis,
given any of the three front-ends.
