Speaker recognition by Gaussian information bottleneck

Abstract

This paper explores a novel approach for the extraction of relevant information in speaker recognition tasks.This approach uses a principled information theoretic frameworkthe Information Bottleneck method (IB).In our application, the method compresses the acoustic data while preserving mostly the relevant information for speaker identification.This paper focuses on a continuous version of the IB method known as the Gaussian Information Bottleneck (GIB).This version assumes that both the source and target variables are high dimensional multivariate Gaussian variables.The GIB was applied in our work to the Super Vector (SV) dimension reduction conundrum.Experiments were conducted on the male part of the NIST SRE 2005 corpora.The GIB representation was compared to other dimension reduction techniques and to a baseline system.In our experiments, the GIB outperformed the baseline system; achieving a 6.1% Equal Error Rate (EER) compared to the 15.1% EER of a baseline system.