Abstract:
This paper proposes a combination of source-normalized
weighted linear discriminant analysis (SN-WLDA) and short utterance
variance (SUV) PLDA modelling to improve the short
utterance PLDA speaker verification. As short-length utterance
i-vectors vary with the speaker, session variations and phonetic
content of the utterance (utterance variation), a combined approach
of SN-WLDA projection and SUV PLDA modelling
is used to compensate the session and utterance variations.
Experimental studies have found that a combination of SNWLDA
and SUV PLDA modelling approach shows an improvement
over baseline system (WCCN[LDA]-projected Gaussian
PLDA (GPLDA)) as this approach effectively compensates the
session and utterance variations.
