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140701 ||| eng |
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|a 9783319071305
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| 100 |
1 |
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|a Rao, K. Sreenivasa
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| 245 |
0 |
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|a Robust Speaker Recognition in Noisy Environments
|h Elektronische Ressource
|c by K. Sreenivasa Rao, Sourjya Sarkar
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| 250 |
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|a 1st ed. 2014
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2014, 2014
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| 300 |
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|a XII, 139 p. 31 illus., 25 illus. in color
|b online resource
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| 505 |
0 |
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|a 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
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Signal processing
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| 700 |
1 |
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|a Sarkar, Sourjya
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-07130-5
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-07130-5?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 621.382
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| 520 |
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|a 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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