Robust Speaker Recognition in Noisy Environments

Nonfiction, Science & Nature, Technology, Imaging Systems, Electronics
Cover of the book Robust Speaker Recognition in Noisy Environments by Sourjya Sarkar, K. Sreenivasa Rao, Springer International Publishing
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Author: Sourjya Sarkar, K. Sreenivasa Rao ISBN: 9783319071305
Publisher: Springer International Publishing Publication: June 21, 2014
Imprint: Springer Language: English
Author: Sourjya Sarkar, K. Sreenivasa Rao
ISBN: 9783319071305
Publisher: Springer International Publishing
Publication: June 21, 2014
Imprint: Springer
Language: English

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.

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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.

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