Speech Emotion Recognition Using Spiking Neural Networks

  • Cosimo A. Buscicchio
  • Przemysław Górecki
  • Laura Caponetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


Human social communication depends largely on exchanges of non-verbal signals, including non-lexical expression of emotions in speech. In this work, we propose a biologically plausible methodology for the problem of emotion recognition, based on the extraction of vowel information from an input speech signal and on the classification of extracted information by a spiking neural network. Initially, a speech signal is segmented into vowel parts which are represented with a set of salient features, related to the Mel-frequency cesptrum. Different emotion classes are then recognized by a spiking neural network and classified into five different emotion classes.


Speech Signal Emotion Recognition Spike Train Interactive Voice Response System Spike Neural Network 
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 2006

Authors and Affiliations

  • Cosimo A. Buscicchio
    • 1
  • Przemysław Górecki
    • 1
  • Laura Caponetti
    • 1
  1. 1.Dipartimento di InformaticaUniversita degli Studi di BariBariItaly

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