15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

PLDA Modeling in the Fishervoice Subspace for Speaker Verification

Jinghua Zhong (1), Weiwu Jiang (1), Wei Rao (2), Man-Wai Mak (2), Helen Meng (1)

(1) Chinese University of Hong Kong, China
(2) Hong Kong Polytechnic University, China

We have previously developed a Fishervoice framework that maps the JFA-mean supervectors into a compressed discriminant subspace using nonparametric Fishers discriminant analysis. It was shown that performing cosine distance scoring (CDS) on these Fishervoice projected vectors (denoted as f-vectors) can outperform the classical joint factor analysis. Unlike the i-vector approach in which the channel variability is suppressed in the classification stage, in the Fishervoice framework, channel variability is suppressed when the f-vectors are constructed. In this paper, we investigate whether channel variability can be further suppressed by performing Gaussian probabilistic discriminant analysis (PLDA) in the classification stage. We also use random subspace sampling to enrich the speaker discriminative information in the f-vectors. Experiments on NIST SRE10 show that PLDA can boost the performance of Fishervoice in speaker verification significantly by a relative decrease of 14.4% in minDCF (from 0.526 to 0.450).

Full Paper

Bibliographic reference.  Zhong, Jinghua / Jiang, Weiwu / Rao, Wei / Mak, Man-Wai / Meng, Helen (2014): "PLDA modeling in the fishervoice subspace for speaker verification", In INTERSPEECH-2014, 1130-1134.