LEADER 01243nam a2200265 i 4500 001 991001722619707536 005 20020507150957.0 008 000210s1991 it ||| | ita 020 $a8813173474 035 $ab11554629-39ule_inst 035 $aLE02723832$9ExL 040 $aDip.to Studi Giuridici$bita 084 $aPR-VII/A 100 1 $aDe Donatis, Alberto$0263423 245 12$aL'autonomia delle parti e la scelta della legge applicabile al contratto internazionale :$bgli ordinamenti interno e convenzionale e il progetto di riforma del 1990 del diritto internazionale privato /$cAlberto De Donatis 260 $aPadova :$bCEDAM,$c1991 300 $axi, 118 p. ;$c25 cm. 490 0 $aPubblicazioni dell'Istituto di studi giuridici. Universitą degli studi G. D'Annunzio, Facoltą di economia e commercio, Pescara 650 4$aContratti internazionali 907 $a.b11554629$b01-03-17$c02-07-02 912 $a991001722619707536 945 $aLE027 PR-VII/A 71$g1$i2027000161324$lle027$o-$pE0.00$q-$rl$sm $t0$u1$v7$w1$x0$y.i11755489$z02-07-02 996 $aAutonomia delle parti e la scelta della legge applicabile al contratto internazionale$9666183 997 $aUNISALENTO 998 $ale027$b01-01-00$cm$da $e-$fita$git $h2$i1 LEADER 01206nam a2200265 i 4500 001 991001414309707536 005 20020502192306.0 008 980224s1997 it ||| | ita 020 $a884640081X 035 $ab11509351-39ule_inst 035 $aPRUMB60526$9ExL 040 $aScuola per assistenti sociali$bita 100 1 $aBulgarelli, Aviana$0117777 245 10$aPolitiche formative e lavoratori in mobilitą :$ble azioni a favore dei lavoratori iscritti nelle liste di mobilitą finanziate dai Fondi Residui della Programmazione Fse (1989-93) nel quadro delle attivitą delle Agenzie per l'impiego /$ca cura di Aviana Bulgarelli e Marinella Giovine 260 $aMilano :$bF. Angeli,$c1997 300 $a442 p. ;$c22 cm. 490 0 $aStrumenti e ricerche ;$v68 650 4$aLavoratori in mobilitą - Formazione 700 1 $aGiovine, Marinella 907 $a.b11509351$b01-03-17$c01-07-02 912 $a991001414309707536 945 $aLE024 SOC/A SR IV 42$g1$i2024000006389$lle021$nex DUSS$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11703829$z01-07-02 996 $aPolitiche formative e lavoratori in mobilitą$9813928 997 $aUNISALENTO 998 $ale021$b01-01-98$cm$da $e-$fita$git $h0$i1 LEADER 03381nam 22006375 450 001 9910299464803321 005 20240228235259.0 010 $a3-319-07130-0 024 7 $a10.1007/978-3-319-07130-5 035 $a(CKB)3710000000134634 035 $a(EBL)1783014 035 $a(SSID)ssj0001274440 035 $a(PQKBManifestationID)11673802 035 $a(PQKBTitleCode)TC0001274440 035 $a(PQKBWorkID)11334869 035 $a(PQKB)10636684 035 $a(MiAaPQ)EBC1783014 035 $a(DE-He213)978-3-319-07130-5 035 $a(PPN)179764438 035 $a(EXLCZ)993710000000134634 100 $a20140621d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRobust Speaker Recognition in Noisy Environments /$fby K. Sreenivasa Rao, Sourjya Sarkar 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (149 p.) 225 1 $aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,$x2191-737X 300 $aDescription based upon print version of record. 311 $a1-322-13629-7 311 $a3-319-07129-7 320 $aIncludes bibliographical references at the end of each chapters. 327 $aRobust 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. 330 $aThis book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models. 410 0$aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,$x2191-737X 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 14$aSignal, Image and Speech Processing. 676 $a006.454 700 $aRao$b K. Sreenivasa$g(Krothapalli Sreenivasa)$4aut$4http://id.loc.gov/vocabulary/relators/aut$01614710 702 $aSarkar$b Sourjya$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299464803321 996 $aRobust Speaker Recognition in Noisy Environments$94085735 997 $aUNINA