Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35637
Title: PLDA modeling in the fishervoice subspace for speaker verification
Authors: Zhong, J
Jiang, W
Rao, W
Mak, MW 
Meng, H
Keywords: Fishervoice
Joint factor analysis
Probabilistic linear discriminant analysis
Random sampling
Supervector
Issue Date: 2014
Publisher: International Speech and Communication Association
Source: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2014, p. 1130-1134 How to cite?
Abstract: 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 ivector 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).
Description: 15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014, 14-18 September 2014
URI: http://hdl.handle.net/10397/35637
ISBN: 9781634394352
Appears in Collections:Conference Paper

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