Support Vector Regression Machine Adopted Speaker Verification System

A speaker verification system based on support vector regression machine (SVR) is presented in this paper. In this paper,we model characteristics of the target speaker by integrating all the mean vectors of Gaussian mixture model (GMM) into a supervector. And then these supervectors are taken to as observations of the Support Vector Regression Machine for classification. This action makes the verification system more robust against outliers or noisy vectors and alleviates the variability of channel affects. Experiments show that the proposed SVR approach outperforms the Support Vector Classification Method at relative reduction of up to 12.8% in equal error ratio (EER) on the NIST 2006 speaker recognition corpus.