Overview
- Discusses the effect of noise, stochastic feature compensation methods based on Gaussian Mixture models (GMMs)
- Demonstrates the standards for speaker databases and noisy environments
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Speech Technology (BRIEFSSPEECHTECH)
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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.
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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
Table of contents (6 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
K. Sreenivasa Rao, Associate Professor, School of Information Technology, Indian Institute of Technology Kharagpur (IIT Kharagpur). Sourjya Sarkar is a graduate student at the Indian Institute of Technology Kharagpur.
Bibliographic Information
Book Title: Robust Speaker Recognition in Noisy Environments
Authors: K. Sreenivasa Rao, Sourjya Sarkar
Series Title: SpringerBriefs in Speech Technology
DOI: https://doi.org/10.1007/978-3-319-07130-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2014
Softcover ISBN: 978-3-319-07129-9Published: 17 July 2014
eBook ISBN: 978-3-319-07130-5Published: 21 June 2014
Series ISSN: 2191-737X
Series E-ISSN: 2191-7388
Edition Number: 1
Number of Pages: XII, 139
Number of Illustrations: 6 b/w illustrations, 25 illustrations in colour