  This paper analyses the short utterance probabilistic linear discriminant
analysis (PLDA) speaker verification with utterance partitioning and short
utterance variance (SUV) modelling approaches. Experimental studies have found
that instead of using single long-utterance as enrolment data, if long enrolled
utterance is partitioned into multiple short utterances and average of short
utterance i-vectors is used as enrolled data, that improves the Gaussian PLDA
(GPLDA) speaker verification. This is because short utterance i-vectors have
speaker, session and utterance variations, and utterance-partitioning approach
compensates the utterance variation. Subsequently, SUV-PLDA is also studied
with utterance partitioning approach, and utterance partitioning-based
SUV-GPLDA system shows relative improvement of 9% and 16% in EER for NIST 2008
and NIST 2010 truncated 10sec-10sec evaluation condition as utterance
partitioning approach compensates the utterance variation and SUV modelling
approach compensates the mismatch between full-length development data and
short-length evaluation data.
