04395nam 22007455 450 991029969350332120250609110125.03-319-17163-110.1007/978-3-319-17163-0(CKB)3710000000379668(EBL)2095432(SSID)ssj0001465584(PQKBManifestationID)11848981(PQKBTitleCode)TC0001465584(PQKBWorkID)11490493(PQKB)10807666(DE-He213)978-3-319-17163-0(MiAaPQ)EBC2095432(PPN)184890632(MiAaPQ)EBC3109203(EXLCZ)99371000000037966820150331d2015 u| 0engur|n|---|||||txtccrLanguage Identification Using Spectral and Prosodic Features /by K. Sreenivasa Rao, V. Ramu Reddy, Sudhamay Maity1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (106 p.)SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737XDescription based upon print version of record.3-319-17162-3 Includes bibliographical references at the end of each chapters. 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).This 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.SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737XSignal processingImage processingSpeech processing systemsNatural language processing (Computer science)Computational linguisticsSignal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Natural Language Processing (NLP)https://scigraph.springernature.com/ontologies/product-market-codes/I21040Computational Linguisticshttps://scigraph.springernature.com/ontologies/product-market-codes/N22000Signal processing.Image processing.Speech processing systems.Natural language processing (Computer science)Computational linguistics.Signal, Image and Speech Processing.Natural Language Processing (NLP).Computational Linguistics.409.54Rao K. Sreenivasa(Krothapalli Sreenivasa),authttp://id.loc.gov/vocabulary/relators/aut0Reddy V. Ramuauthttp://id.loc.gov/vocabulary/relators/autMaity Sudhamayauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910299693503321Language Identification Using Spectral and Prosodic Features4117402UNINA