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 2s, showed that our method consistently outperforms other approaches yielding relative improvements of 2051% and 1530% over baseline GMM and the LDA-GMM systems, respectively.
short-segment speaker identification speaker model construction feature vector selection linear discriminant analysis (lda)