Robust speaker identification based on selective use of feature vectors

A new method for speaker identification that selectively uses feature vectors for robust decision-making is described. Experimental results, with short speech segments ranging from 0.25 to 2 s, showed that our method consistently outperforms other approaches yielding relative improvements of 20–51% and 15–30% over baseline GMM and the LDA-GMM systems, respectively.