Abstract:
This paper investigates advanced channel compensation techniques for the purpose of improving i-vector speaker
verification performance in the presence of high intersession variability using the NIST 2008 and 2010 SRE corpora.
The performance of four channel compensation techniques: (a) weighted maximum margin criterion (WMMC),
(b) source-normalized WMMC (SN-WMMC), (c) weighted linear discriminant analysis (WLDA) and (d) sourcenormalized
WLDA (SN-WLDA) have been investigated. We show that, by extracting the discriminatory information
between pairs of speakers as well as capturing the source variation information in the development i-vector space, the
SN-WLDA based cosine similarity scoring (CSS) i-vector system is shown to provide over 20% improvement in EER
for NIST 2008 interview and microphone verification and over 10% improvement in EER for NIST 2008 telephone verification,
when compared to SN-LDA based CSS i-vector system. Further, score-level fusion techniques are analyzed
to combine the best channel compensation approaches, to provide over 8% improvement in DCF over the best single
approach, (SN-WLDA), for NIST 2008 interview/ telephone enrolment-verification condition. Finally, we demonstrate
that the improvements found in the context of CSS also generalize to state-of-the-art GPLDA with up to 14% relative
improvement in EER for NIST SRE 2010 interview and microphone verification and over 7% relative improvement in
EER for NIST SRE 2010 telephone verification.
