Enhanced VQ-Based Algorithms for Speech Independent Speaker Identification

Weighted distance measure and discriminative training are two different approaches to enhance VQ-based solutions for speaker identification. To account for varying importance of the LPC coefficients in SV, the so-called partition normalized distance measure successfully used normalized feature components. This paper introduces an alternative, called heuristic weighted distance, to lift up higher order MFCC feature vector components using a linear formula. Then it proposes two new algorithms combining the heuristic weighting and the partition normalized distance measure with group vector quantization discriminative training to take advantage of both approaches. Experiments using the TIMIT corpus suggest that the new combined approach is superior to current VQ-based solutions (50% error reduction). It also outperforms the Gaussian Mixture Model using the Wavelet features tested in a similar setting.