Abstract:
This paper introduces the Weighted Linear Discriminant Analysis
(WLDA) technique, based upon the weighted pairwise Fisher
criterion, for the purposes of improving i-vector speaker verification
in the presence of high inter-session variability. By taking advantage
of the speaker discriminative information that is available
in the distances between pairs of speakers clustered in the development
i-vector space, the WLDA technique is shown to provide an improvement
in speaker verification performance over traditional Linear
Discriminant Analysis (LDA) approaches. A similar approach
is also taken to extend the recently developed Source Normalised
LDA (SNLDA) into Weighted SNLDA (WSNLDA) which, similarly,
shows an improvement in speaker verification performance
in both matched and mismatched enrolment/verification conditions.
Based upon the results presented within this paper using the NIST
2008 Speaker Recognition Evaluation dataset, we believe that both
WLDA and WSNLDA are viable as replacement techniques to improve
the performance of LDA and SNLDA-based i-vector speaker
verification.
