Compensating inter-dataset variability in PLDA hyper-parameters for robust speaker recognition

Abstract

Recently we have introduced a method named inter-dataset variability compensation (IDVC) in the context of speaker recognition in a mismatched dataset. IDVC compensates dataset shifts in the i-vector space by constraining the shifts to a low dimensional subspace. The subspace is estimated from a heterogeneous development set which is partitioned into homogenous subsets. In this work we generalize the IDVC method to compensate inter-dataset variability attributed to additional PLDA hyper-parameters, namely the within and between speaker covariance matrices. Using the proposed method we managed to recover 85% of the degradation due to mismatched PLDA training in the framework of the JHU-2013 domain adaptation challenge.