  Linear Discriminant Analysis (LDA) has been used as a standard
post-processing procedure in many state-of-the-art speaker recognition tasks.
Through maximizing the inter-speaker difference and minimizing the
intra-speaker variation, LDA projects i-vectors to a lower-dimensional and more
discriminative sub-space. In this paper, we propose a neural network based
compensation scheme(termed as deep discriminant analysis, DDA) for i-vector
based speaker recognition, which shares the spirit with LDA. Optimized against
softmax loss and center loss at the same time, the proposed method learns a
more compact and discriminative embedding space. Compared with the Gaussian
distribution assumption of data and the learnt linear projection in LDA, the
proposed method doesn't pose any assumptions on data and can learn a non-linear
projection function. Experiments are carried out on a short-duration
text-independent dataset based on the SRE Corpus, noticeable performance
improvement can be observed against the normal LDA or PLDA methods.
