PLDA Modeling in I-Vector and Supervector Space for Speaker Verification

In this paper, we advocate the use of uncompressed form of i-vector. We employ the probabilistic linear discriminant analysis (PLDA) to handle speaker and session variability for speaker verification task. An i-vector is a low-dimensional vector containing both speaker and channel information acquired from a speech segment. When PLDA is used on i-vector, dimension reduction is performed twice - first in the i-vector extraction process and second in the PLDA model. Keeping the full dimensionality of i-vector in the supervector space for PLDA modeling and scoring would avoid unnecessary loss of information. The drawback of using PLDA on uncompressed i-vector is the inversion of large matrices, which we show can be solved rather efficiently by portioning large matrix into smaller blocks. We also introduce the Gaussianized rank-norm, as an alternative to whitening, for feature normalization prior to PLDA modeling.