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Robust Speaker Recognition in Noisy Environments [electronic resource] / by K. Sreenivasa Rao, Sourjya Sarkar.

By: Rao, K. Sreenivasa [author.].
Contributor(s): Sarkar, Sourjya [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Electrical and Computer Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XII, 139 p. 31 illus., 25 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319071305.Subject(s): Engineering | Engineering | Signal, Image and Speech ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Robust Speaker Verification - A Review -- Speaker Verification in Noisy Environments using Gaussian Mixture Models -- Stochastic Feature Compensation for Robust Speaker Verification -- Robust Speaker Modeling for Speaker Verification in Noisy Environments.
In: Springer eBooksSummary: 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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Robust Speaker Verification - A Review -- Speaker Verification in Noisy Environments using Gaussian Mixture Models -- Stochastic Feature Compensation for Robust Speaker Verification -- Robust Speaker Modeling for Speaker Verification in 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.

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