Speaker discriminative weighting method for VQ-based speaker identification

We consider the matching function in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the similarity of the unknown speaker and the models in the database. In this paper, we propose to use weighted matching method that takes into account the correlations between the known models in the database. Larger weights are assigned to vectors that have high discriminating power between the speakers and vice versa. Experiments show that the new method provides significantly higher identification accuracy and it can detect the correct speaker from shorter speech samples more reliable than the unweighted matching method.