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Discusses the effect of noise, stochastic feature compensation methods based on Gaussian Mixture models (GMMs)
Demonstrates the standards for speaker databases and noisy environments
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.
Content Level »Research
Keywords »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