Connectionist models often offer good performance in pattern recognition and generalization, and present such qualities as natural learning ability, noise tolerance and graceful degradation. By contrast, symbolic models often present a complementary profile: they offer good performance in reasoning and deduction, and present such qualities as natural symbolic manipulation and explanation abilities. In the context of this paper, we address two limitations of artificial neural networks: the lack of explicit knowledge and the absence of temporal aspect in their implementation. STN : is a model of a specialized temporal neuron which includes both symbolic and temporal aspects. To illustrate the STN utility, we consider a system for phoneme recognition. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Bahi, H., & Sellami, M. (2005). Neural expert model applied to phonemes recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3587 LNAI, pp. 507–515). Springer Verlag. https://doi.org/10.1007/11510888_50
Mendeley helps you to discover research relevant for your work.