Abstract:
This paper investigates the use of the dimensionality-reduction
techniques weighted linear discriminant analysis (WLDA), and
weighted median fisher discriminant analysis (WMFD), before
probabilistic linear discriminant analysis (PLDA) modeling for
the purpose of improving speaker verification performance in
the presence of high inter-session variability. Recently it was
shown that WLDA techniques can provide improvement over
traditional linear discriminant analysis (LDA) for channel compensation
in i-vector based speaker verification systems. We
show in this paper that the speaker discriminative information
that is available in the distance between pair of speakers clustered
in the development i-vector space can also be exploited
in heavy-tailed PLDA modeling by using the weighted discriminant
approaches prior to PLDA modeling. Based upon
the results presented within this paper using the NIST 2008
Speaker Recognition Evaluation dataset, we believe that WLDA
and WMFD projections before PLDA modeling can provide an
improved approach when compared to uncompensated PLDA
modeling for i-vector based speaker verification systems.
