Parameter sharing and minimum classification error training of mixtures of factor analyzers for speaker identification

This paper investigates the parameter tying strategies of mixtures of factor analyzers (MFA) and discriminative training of MFA for speaker identification. The parameters of factor loading matrices or diagonal matrices are shared in different mixtures of MFA. The minimum classification error (MCE) training is applied to the MFA parameters to enhance the discrimination abilities. The results of text-independent speaker identification experiments show that MFA outperforms the conventional Gaussian mixture models (GMM) with diagonal or full covariance matrices and achieves the best performance when sharing the diagonal matrices, resulting in a relative gain of 26% over the GMM with diagonal covariance matrices. The recognition performance is further improved by the MCE training with an additional 3% error reduction.