Abstract:
Experimental studies have found that when the state-of-theart
probabilistic linear discriminant analysis (PLDA) speaker
verification systems are trained using out-domain data, it significantly
affects speaker verification performance due to the
mismatch between development data and evaluation data. To
overcome this problem we propose a novel unsupervised inter
dataset variability (IDV) compensation approach to compensate
the dataset mismatch. IDV-compensated PLDA system
achieves over 10% relative improvement in EER values
over out-domain PLDA system by effectively compensating
the mismatch between in-domain and out-domain data.
