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.
Keywords
- 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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© 2006 Springer-Verlag Berlin Heidelberg
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Salem, Z.N.B., Bougrain, L., Alexandre, F. (2006). Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition. In: Schwenker, F., Marinai, S. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2006. Lecture Notes in Computer Science(), vol 4087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11829898_2
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DOI: https://doi.org/10.1007/11829898_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37951-5
Online ISBN: 978-3-540-37952-2
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