Improving speaker identification via Singular Value Decomposition based Feature Transformer

State-of-the-art Speaker Identification (SI) systems use Gaussian Mixture Models (GMM) for modeling speakerspsila data. Using GMM, a speaker can be identified accurately even from a large number of speakers, when model complexity is large. However, lower ordered speaker model using GMM show poor accuracy as lesser number of Gaussian are involved. In SI context, not much attention have been paid towards improving accuracies for lower order models although they have been used in real-time applications like hierarchical speaker pruning. In this paper, two different approaches have been proposed using Singular Value Decomposition (SVD) based Feature Transformer (FT) for improving accuracies especially for lower ordered speaker models. The results show significant improvements over baseline and have been presented on two widely different public databases comprising of more than 130 speakers.