• DocumentCode
    1899775
  • Title

    A comprehensive survey on features and methods for speech emotion detection

  • Author

    Alva, M. Yashaswi ; Nachamai, M. ; Paulose, Joy

  • Author_Institution
    Dept. of Comput. Sci., Christ Univ., Bangalore, India
  • fYear
    2015
  • fDate
    5-7 March 2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Human computer interaction will be natural and effective when the interfaces are sensitive to human emotion or stress. Previous studies were mainly focused on facial emotion recognition but speech emotion detection is gaining importance due its wide range of applications. Speech emotion recognition still remains a challenging task in the field of affective computing as no defined standards exist for emotion classification. Speech signal carries large information related to the emotions conveyed by a person. Speech recognition system fails miserably if robust techniques are not implemented to address the variations in speech due to emotion. Emotion detection from speech has two main steps. They are feature extraction and classification. The goal of this paper is to give an overview on the types of corpus, features and classification techniques that are associated with speech emotion recognition.
  • Keywords
    emotion recognition; feature extraction; human computer interaction; speech recognition; emotion classification; facial emotion recognition; feature classification technique; feature extraction; human computer interaction; human emotion; speech emotion detection; speech recognition system; speech signal; Emotion recognition; Hidden Markov models; Mel frequency cepstral coefficient; Silicon; Speech; Speech recognition; Support vector machines; Emotion recognition; classification methods; speech corpus; speech features;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on
  • Conference_Location
    Coimbatore
  • Print_ISBN
    978-1-4799-6084-2
  • Type

    conf

  • DOI
    10.1109/ICECCT.2015.7226047
  • Filename
    7226047