Improved i-vector speaker verification based on WCCN and ZT-norm

Abstract

For the purpose of improving system performance in high channel variability, an improved i-vector speaker verification algorithm is proposed in this paper. Firstly, i-vectors are obtained from GMM-UBM of registered speakers. And then, the weighted linear discriminant analysis is utilized to play the role of channel compensation and dimensionality reduction in i-vectors. By doing this, more discriminant vectors could be extracted. Immediately following, WCCN and ZT-norm are combined to normalize the scores from cosine distance score classifier for the sake of removing channel disturbance. Finally, cosine distance score classifier of high robustness is generated to find target speaker. Experiment results demonstrate that our proposed i-vector system has better performance.