Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model
Reduced computation in an i-vector PLDA based speaker recognition system. Employ a tree-structured universal background model. Hash GMM is formed by using Gaussian reduction post SR system development. Computational reductions of >40X are demonstrated with 2.21% degradation in EER. Achieve 15X computational reduction with negligible degradation in equal error rate.
speaker recognition clustering methods tree graphs