Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition

  • Zouhour Neji Ben Salem
  • Laurent Bougrain
  • Frédéric Alexandre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4087)


In this paper, we compare two models biologically inspired and gathering spatio-temporal data coding, representation and processing. These models are based on Self-Organizing Map (SOM) yielding to a Spatio-Temporel Organization Map (STOM). More precisely, the map is trained using two different spatio-temporal algorithms taking their roots in biological researches: The ST-Kohonen and the Time-Organized Map (TOM). These algorithms use two kinds of spatio-temporal data coding. The first one is based on the domain of complex numbers, while the second is based on the ISI (Inter Spike Interval). STOM is experimented in the field of speech recognition in order to evaluate its performance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.


Speech Recognition Speech Signal Automatic Speech Recognition Inter Spike Interval Digit 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 2006

Authors and Affiliations

  • Zouhour Neji Ben Salem
    • 1
  • Laurent Bougrain
    • 2
  • Frédéric Alexandre
    • 2
  1. 1.AI Unit, CRISTAL LaratoryNational School of Computer SciencesTunisia
  2. 2.Cortex Team, LORIA LaboratoryNancyFrance

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