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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Table of contents (6 chapters)
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Front Matter
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Back Matter
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
About this book
Authors and Affiliations
About the authors
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
Industry Sectors: Aerospace, Electronics, Engineering, IT & Software, Telecommunications