Speaker verification using Fisher vector

This paper introduces an approach based on Fisher vector feature representation for speaker verification. The Fisher vector is originated from Fisher Kernel and represents each utterance as a high-dimensional vector by encoding the derivatives of the loglikelihood of the UBM model with respect to it´s mean and variances. This representation captures the average first and second order differences between the utterance and each of the Gaussian centers of the UBM model. And the Fisher vector is further projected to a low-dimensional space using PPCA which is conducted in a similar way of factor analysis. We compare the proposed method with the state-of-art i-vector approach on the telephone-telephone condition of NIST SRE2010 female and male core task. The experimental results indicate that the proposed Fisher vector based method is competitive with i-vector. It can also provide complementary information to i-vector and the fusion of these two approach obtains a relative improvement of 11.8% and 14.7% in EER and 9.2% and 2.7% in minDCF for female and male than i-vector alone.