Comparison between two spatio-temporal organization maps for speech recognition

Authors:
Zouhour Neji Ben Salem;Laurent Bougrain;Frédéric Alexandre
Affiliations:
AI Unit, CRISTAL Laratory, National School of Computer Sciences, Tunisia;Cortex Team, LORIA Laboratory, Nancy, France;Cortex Team, LORIA Laboratory, Nancy, France
Venue:
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Year:
2006

Citing 3
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Abstract

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.