LEADER 04395nam 22007455 450 001 9910299693503321 005 20250609110125.0 010 $a3-319-17163-1 024 7 $a10.1007/978-3-319-17163-0 035 $a(CKB)3710000000379668 035 $a(EBL)2095432 035 $a(SSID)ssj0001465584 035 $a(PQKBManifestationID)11848981 035 $a(PQKBTitleCode)TC0001465584 035 $a(PQKBWorkID)11490493 035 $a(PQKB)10807666 035 $a(DE-He213)978-3-319-17163-0 035 $a(MiAaPQ)EBC2095432 035 $a(PPN)184890632 035 $a(MiAaPQ)EBC3109203 035 $a(EXLCZ)993710000000379668 100 $a20150331d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLanguage Identification Using Spectral and Prosodic Features /$fby K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (106 p.) 225 1 $aSpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,$x2191-737X 300 $aDescription based upon print version of record. 311 08$a3-319-17162-3 320 $aIncludes bibliographical references at the end of each chapters. 327 $a Introduction.- Literature Review -- Language Identification using Spectral Features -- Language Identification using Prosodic Features -- Summary and Conclusions -- Appendix A: LPCC Features -- Appendix B: MFCC Features --  Appendix C: Gaussian Mixture Model (GMM). 330 $aThis book discusses the impact of spectral features extracted from frame level, glottal closure regions, and pitch-synchronous analysis on the performance of language identification systems. In addition to spectral features, the authors explore prosodic features such as intonation, rhythm, and stress features for discriminating the languages. They present how the proposed spectral and prosodic features capture the language specific information from two complementary aspects, showing how the development of language identification (LID) system using the combination of spectral and prosodic features will enhance the accuracy of identification as well as improve the robustness of the system. This book provides the methods to extract the spectral and prosodic features at various levels, and also suggests the appropriate models for developing robust LID systems according to specific spectral and prosodic features. Finally, the book discuss about various combinations of spectral and prosodic features, and the desired models to enhance the performance of LID systems. 410 0$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 $a409.54 700 $aRao$b K. Sreenivasa$g(Krothapalli Sreenivasa),$4aut$4http://id.loc.gov/vocabulary/relators/aut$00 702 $aReddy$b V. Ramu$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMaity$b Sudhamay$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299693503321 996 $aLanguage Identification Using Spectral and Prosodic Features$94117402 997 $aUNINA