IDEAS home Printed from https://ideas.repec.org/a/igg/jse000/v7y2016i1p58-68.html
   My bibliography  Save this article

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

Author

Listed:
  • Imen Trabelsi

    (Sciences and Technologies of Image and Telecommunications (SETIT), University of Sfax, Tunisia)

  • Med Salim Bouhlel

    (Sciences and Technologies of Image and Telecommunications (SETIT), University of Sfax, Tunisia)

Abstract

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

Suggested Citation

  • Imen Trabelsi & Med Salim Bouhlel, 2016. "Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition," International Journal of Synthetic Emotions (IJSE), IGI Global, vol. 7(1), pages 58-68, January.
    • Handle: RePEc:igg:jse000:v:7:y:2016:i:1:p:58-68
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSE.2016010105
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jse000:v:7:y:2016:i:1:p:58-68. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.