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The GMM-SVM Supervector Approach for the Recognition of the Emotional Status from Speech

  • Friedhelm Schwenker
  • Stefan Scherer
  • Yasmine M. Magdi
  • Günther Palm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

Emotion recognition from speech is an important field of research in human-machine-interfaces, and has various applications, for instance for call centers. In the proposed classifier system RASTA-PLP features (perceptual linear prediction) are extracted from the speech signals. The first step is to compute an universal background model (UBM) representing a general structure of the underlying feature space of speech signals. This UBM is modeled as a Gaussian mixture model (GMM). After computing the UBM the sequence of feature vectors extracted from the utterance is used to re-train the UBM. From this GMM the mean vectors are extracted and concatenated to the so-called GMM supervectors which are then applied to a support vector machine classifier. The overall system has been evaluated by using utterances from the public Berlin emotional database. Utilizing the proposed features a recognition rate of 79% (utterance based) has been achieved which is close to the performance of humans on this database.

Keywords

Support Vector Machine Speech Signal Gaussian Mixture Model Emotion Recognition Automatic Speech Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Friedhelm Schwenker
    • 1
  • Stefan Scherer
    • 1
  • Yasmine M. Magdi
    • 2
  • Günther Palm
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany
  2. 2.Computer Science and Engineering DepartmentGerman University in CairoHeliopolisEgypt

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