A study on GMM-SVM with adaptive relevance factor and its comparison with i-vector and JFA for speaker recognition

Recently, joint factor analysis (JFA) and identity-vector (i-vector) represent the dominant techniques used for speaker recognition due to their superior performance. Developed relatively earlier, the Gaussian mixture model - support vector machine (GMM-SVM) with nuisance attribute projection (NAP) has gradually become less popular. However, when developing the relevance factor in maximum a posteriori (MAP) estimation of GMM to be adapted by application data in place of the conventional fixed value, it is noted that GMM-SVM demonstrates some advantages. In this paper, we conduct a comparative study between GMM-SVM with adaptive relevance factor and JFA/i-vector under the framework of Speaker Recognition Evaluation (SRE) formulated by the National Institute of Standards and Technology (NIST).