Probabilistic Linear Discriminant Analysis (PLDA) has become state-of-the-art
method for modeling i-vector space in speaker recognition task. However
the performance degradation is observed if enrollment data size differs
from one speaker to another. This paper presents a solution to such
problem by introducing new PLDA scoring normalization technique. Normalization
parameters are derived in a blind way, so that, unlike traditional
ZT-norm, no extra development data is required. Moreover, proposed method has shown to be optimal in terms of detection cost function. The experiments conducted on NIST SRE 2014 database demonstrate an improved accuracy in a mixed enrollment number condition.
Bibliographic reference. Doroshin, Danila / Lubimov, Nikolay / Nastasenko, Marina / Kotov, Mikhail (2015): "Blind score normalization method for PLDA based speaker recognition", In INTERSPEECH-2015, 210-213.