Enhanced VQ-Based Algorithms for Speech Independent Speaker Identification

Abstract

Weighted distance measure and discriminative training are two different directions to enhance VQ-based solutions for speaker identification. In the first direction, the partition normalized distance measure successfully used normalized feature components to account for varying importance of the LPC coefficients. In the second direction, the group vector quantization speeded up discriminative training by randomly selecting a group of vectors as a training unit in each learning step. This paper introduces an alternative, called heuristic weighted distance, to linearly lift up higher order MFCC feature vector components. Then two new algorithms are proposed to combine the heuristic weighted distance and the partition normalized distance measure with the group vector quantization to take full advantage of both directions. Testing on the TIMIT and NTIMIT corpora showed that the proposed methods are superior to current VQ-based solutions, and are in a comparable range to the Gaussian Mixture Model using the Wavelet or MFCC features.