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Autor(en): 
  • K. Sreenivasa Rao
  • Dipanjan Nandi
  • Language Identification Using Excitation Source Features 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  April 2015  
    Genre:  Naturwissensch., Medizin, Technik 
     
    C / Computational Linguistics / Computerlinguistik und Korpuslinguistik / Digitale Signalverarbeitung (DSP) / engineering / for LID / Image processing / Natural language & machine translation
    ISBN:  9783319177243 
    EAN-Code: 
    9783319177243 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  SpringerBriefs in Speech Technology  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  2117 gr 
    Seiten:  119 
    Illustration:  XII, 119 p. 19 illus., 3 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This 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.

      



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