Titlebook: Robust Speaker Recognition in Noisy Environments; K. Sreenivasa Rao,Sourjya Sarkar Book 2014 The Author(s) 2014 Feature Compensation using

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Book 2014odels, (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 Modeling for Speaker Verification in Noisy Environments, (SVMs)). For improving the performance of the proposed speaker verification systems, utterance partitioning methods are used. The discussion is closely followed by state-of-the-art variants of GMM supervector based approaches (i.e., i-vectors) and algorithms for combining robust classifiers.

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Speaker Verification in Noisy Environments Using Gaussian Mixture Models,an acoustic modeling framework (namely GMM-UBM) using speaker-dependent GMMs and a speaker-independent Universal Background Model (UBM), is studied for simulated noisy backgrounds. Significance of a feature mapping technique using multiple UBMs for compensating background noise is explored. The spea