Abstract:
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 enrolledutterance
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 utterancepartitioning-
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 utterancepartitioning
approach compensates the utterance variation and
SUV modelling approach compensates the mismatch between
full-length development data and short-length evaluation data.
