Combining selection tree with observation reordering pruning for efficient speaker identification using GMM-UBM

In this paper a new method of reducing the computational load for Gaussian mixture model universal background model (GMM-UBM) based speaker identification is proposed. In order to speed up the selection of N-best Gaussian mixtures in a UBM, a selection tree (ST) structure as well as relevant operations is proposed. Combined with the existing observation reordering pruning (ORP) method which was proposed for rapid pruning of unlikely speaker model candidates, the proposed method achieves a much larger computation reduction factor than any single individual method. Experimental results show that a GMM-UBM system used in a conjunction with ST and ORP can speed up the computation by a factor of about 16 with an error rate increase of only about 1% compared with a baseline GMM-UBM system.