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.