• DocumentCode
    3353827
  • Title

    Determining Efficiency of Speech Feature Groups in Emotion Detection

  • Author

    Polat, G. ; Altun, Hallis

  • Author_Institution
    Nigde Univ. Yuksek Lisans Ogrencisi, Nigde
  • fYear
    2007
  • fDate
    11-13 June 2007
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Features, extract from speech parameter are frequently used in emotion detection problem. Prosodic, MFCC, LPC and band energy feature groups are commonly used in literature to detect emotion in speech. The aim of the study is to examine the efficiency of these features groups in emotion detection problem using a SVM classifier.
  • Keywords
    emotion recognition; feature extraction; speech recognition; support vector machines; SVM classifier; emotion detection; speech feature groups; speech parameter; Feature extraction; Linear predictive coding; Mel frequency cepstral coefficient; Speech; Support vector machine classification; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th
  • Conference_Location
    Eskisehir
  • Print_ISBN
    1-4244-0719-2
  • Electronic_ISBN
    1-4244-0720-6
  • Type

    conf

  • DOI
    10.1109/SIU.2007.4298582
  • Filename
    4298582
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3353827