About this book
This book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.
Feature Compensation using Multiple Background Models Robust Speaker Recognition in Noisy Environment Robust Speaker Recognition using I-vectors Robust Speaker Verification using GMM-SVM Framework Speaker Recognition in Noisy Background Speaker Recognition in Varying Background Speaker Verification in Noisy Background Speaker Verification using Super-vectors Stochastic Feature Compensation for Robust Speaker Recognition Total Variability Modeling for Robust Speaker Recognition