LEADER 04535nam 22007095 450 001 9910299821303321 005 20240228235017.0 010 $a3-319-17725-7 024 7 $a10.1007/978-3-319-17725-0 035 $a(CKB)3710000000399958 035 $a(EBL)2095434 035 $a(SSID)ssj0001501227 035 $a(PQKBManifestationID)11918664 035 $a(PQKBTitleCode)TC0001501227 035 $a(PQKBWorkID)11522611 035 $a(PQKB)10374413 035 $a(DE-He213)978-3-319-17725-0 035 $a(MiAaPQ)EBC2095434 035 $a(PPN)185486908 035 $a(EXLCZ)993710000000399958 100 $a20150415d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLanguage Identification Using Excitation Source Features /$fby K. Sreenivasa Rao, Dipanjan Nandi 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (128 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 $a3-319-17724-9 327 $aIntroduction -- Language Identification--A Brief Review -- Implicit Excitation Source Features for Language Identification -- Parametric Excitation Source Features for Language Identification -- Complementary and Robust Nature of Excitation Source Features for Language Identification -- Conclusion. 330 $aThis book discusses the contribution of excitation source information in discriminating language. The authors focus on the excitation source component of speech for enhancement of language identification (LID) performance. Language specific features are extracted using two different modes: (i) Implicit processing of linear prediction (LP) residual and (ii) Explicit parameterization of linear prediction residual. The book discusses how in implicit processing approach, excitation source features are derived from LP residual, Hilbert envelope (magnitude) of LP residual and Phase of LP residual; and in explicit parameterization approach, LP residual signal is processed in spectral domain to extract the relevant language specific features. The authors further extract source features from these modes, which are combined for enhancing the performance of LID systems. The proposed excitation source features are also investigated for LID in background noisy environments. Each chapter of this book provides the motivation for exploring the specific feature for LID task, and subsequently discuss the methods to extract those features and finally suggest appropriate models to capture the language specific knowledge from the proposed features. Finally, the book discuss about various combinations of spectral and source features, and the desired models to enhance the performance of LID systems. 410 0$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 $aNatural language processing (Computer science) 606 $aComputational linguistics 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aNatural Language Processing (NLP)$3https://scigraph.springernature.com/ontologies/product-market-codes/I21040 606 $aComputational Linguistics$3https://scigraph.springernature.com/ontologies/product-market-codes/N22000 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aNatural language processing (Computer science). 615 0$aComputational linguistics. 615 14$aSignal, Image and Speech Processing. 615 24$aNatural Language Processing (NLP). 615 24$aComputational Linguistics. 676 $a006.35 676 $a410.285 676 $a620 676 $a621.382 700 $aRao$b K. Sreenivasa$g(Krothapalli Sreenivasa)$4aut$4http://id.loc.gov/vocabulary/relators/aut$01614710 702 $aNandi$b Dipanjan$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299821303321 996 $aLanguage Identification Using Excitation Source Features$93944612 997 $aUNINA