Speaker identification based on discriminative vector quantization

A novel discriminative vector quantization method for speaker identification (DVQSI) is proposed, and its parameters selection is discussed. The vector space of speech features is divided into a number of subspaces and the distribution of the inter speaker variation inside the speech feature vector space is considered. Discriminative weighted average distortion instead of equally weighted average distortion is used in speaker identification (SI). The proposed approach can be considered a generalization of the existing vector quantization (VQ) technique and the experimental results confirm the improved SI accuracy