• DocumentCode
    3113224
  • Title

    Analysis of Speech Features for Emotion Detection: A Review

  • Author

    Sudhakar, Rode Snehal ; Anil, Manjare Chandraprabha

  • Author_Institution
    Dept. of Electron. & Telecomunication Eng., JSPM´s Jaywantrao Sawant Coll. of Eng., Pune, India
  • fYear
    2015
  • fDate
    26-27 Feb. 2015
  • Firstpage
    661
  • Lastpage
    664
  • Abstract
    Emotion detection of speech in human machine interaction is very important. Framework for emotion detection is essential, that includes various modules performing actions like speech to text conversion, feature extraction, feature selection and classification of those features to identify the emotions. The features used for emotion detection of speech are prosody features, spectral features and voice quality features. The classifications of features involve the training of various emotional models to perform the classification appropriately. The features selected to be classified must be salient to detect the emotions correctly. And these features should have to convey the measurable level of emotional modulation.
  • Keywords
    emotion recognition; feature extraction; feature selection; spectral analysis; text analysis; emotion detection; emotional modulation; feature classification; feature extraction; feature selection; human machine interaction; prosody features; spectral features; speech features; text conversion; voice quality features; Acoustics; Databases; Emotion recognition; Feature extraction; Hidden Markov models; Speech; Speech recognition; Classifier; GMM; HMM; KLD; Prosody; pitch contour;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on
  • Conference_Location
    Pune
  • Type

    conf

  • DOI
    10.1109/ICCUBEA.2015.135
  • Filename
    7155930