An Integration of Random Subspace Sampling and Fishervoice for Speaker Verification

In this paper, we propose an integration of random subspace sampling and Fishervoice for speaker verification. In the previous random sampling framework [1], we randomly sample the JFA feature space into a set of low-dimensional subspaces. For every random subspace, we use Fishervoice to model the intrinsic vocal characteristics in a discriminant subspace. The complex speaker characteristics are modeled through multiple subspaces. Through a fusion rule, we form a more powerful and stable classifier that can preserve most of the discriminative information. But in many cases, random subspace sampling may discard too much useful discriminative information for high-dimensional feature space. Instead of increasing the number of random subspace or using more complex fusion rules which increase system complexity, we attempt to increase the performance of each individual weak classifier. Hence, we propose to investigate the integration of random subspace sampling with the Fishervoice approach. The proposed new framework is shown to provide better performance in both NIST SRE08 and NIST SRE10 evaluation corpora. Besides, we also apply Probabilistic Linear Discriminant Analysis (PLDA) on the supervector space for comparision. Our proposed framework can improve PLDA performance by a relative decrease of 12.47% in EER and reduced the minDCF from 0.0216 to 0.0210.