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Robust Speaker Recognition in Noisy Environments / / by K. Sreenivasa Rao, Sourjya Sarkar



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Autore: Rao K. Sreenivasa (Krothapalli Sreenivasa) Visualizza persona
Titolo: Robust Speaker Recognition in Noisy Environments / / by K. Sreenivasa Rao, Sourjya Sarkar Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014
Edizione: 1st ed. 2014.
Descrizione fisica: 1 online resource (149 p.)
Disciplina: 006.454
Soggetto topico: Signal processing
Image processing
Speech processing systems
Signal, Image and Speech Processing
Persona (resp. second.): SarkarSourjya
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references at the end of each chapters.
Nota di contenuto: 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.
Sommario/riassunto: 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.
Titolo autorizzato: Robust Speaker Recognition in Noisy Environments  Visualizza cluster
ISBN: 3-319-07130-0
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910299464803321
Lo trovi qui: Univ. Federico II
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Serie: SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, . 2191-737X