Comparison between two spatio-temporal organization maps for speech recognition

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 perform-ance for such time variable application and to prove that biological models are capable of giving good results as stochastic and hybrid ones.
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Communication dans un congrès
Second IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition - ANNPR 2006, Aug 2006, University of Ulm/Germany, Springer Verlag, 4087, pp.11-20, 2006, Lecture Notes in Computer Science. 〈10.1007/11829898_2〉
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Contributeur : Laurent Bougrain <>
Soumis le : jeudi 5 octobre 2006 - 20:55:32
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48
Document(s) archivé(s) le : samedi 14 mai 2011 - 00:06:38

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Zouhour Neji Ben Salem, Laurent Bougrain, Frédéric Alexandre. Comparison between two spatio-temporal organization maps for speech recognition. Second IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition - ANNPR 2006, Aug 2006, University of Ulm/Germany, Springer Verlag, 4087, pp.11-20, 2006, Lecture Notes in Computer Science. 〈10.1007/11829898_2〉. 〈inria-00104156〉

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