01037nam--2200325---450-99000369842020331620121012122551.0000369842USA01000369842(ALEPH)000369842USA0100036984220121012d1993----km-y0itay50------baitaIT||||||||001yy<<Le>> confraternite dell'arcidiocesi di Palermostoria e artea cura di Maria Concetta Di Natalefotografie di Enzo BraiPalermoEdi OFTESstampa 1993353 p.ill.20 cmConfraternitePalermoCataloghi di esposizioniBNCF704.948209458231DI NATALE,Maria ConcettaBRAI,EnzoITsalbcISBD990003698420203316XII.2.A. 815237884 L.M.XII.2.A.00312756BKUMAIANNONE9020121012USA011225Confraternite dell'Arcidiocesi di Palermo1094919UNISA03381nam 22006375 450 991029946480332120240228235259.03-319-07130-010.1007/978-3-319-07130-5(CKB)3710000000134634(EBL)1783014(SSID)ssj0001274440(PQKBManifestationID)11673802(PQKBTitleCode)TC0001274440(PQKBWorkID)11334869(PQKB)10636684(MiAaPQ)EBC1783014(DE-He213)978-3-319-07130-5(PPN)179764438(EXLCZ)99371000000013463420140621d2014 u| 0engur|n|---|||||txtccrRobust Speaker Recognition in Noisy Environments /by K. Sreenivasa Rao, Sourjya Sarkar1st ed. 2014.Cham :Springer International Publishing :Imprint: Springer,2014.1 online resource (149 p.)SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737XDescription based upon print version of record.1-322-13629-7 3-319-07129-7 Includes bibliographical references at the end of each chapters.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.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.SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737XSignal processingImage processingSpeech processing systemsSignal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Signal processing.Image processing.Speech processing systems.Signal, Image and Speech Processing.006.454Rao K. Sreenivasa(Krothapalli Sreenivasa)authttp://id.loc.gov/vocabulary/relators/aut1614710Sarkar Sourjyaauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910299464803321Robust Speaker Recognition in Noisy Environments4085735UNINA