Probabilistic Self-Organizing Maps for Text-Independent Speaker Identification

The present paper introduces a novel speaker modeling technique for text-independent speaker identification using probabilistic self-organizing maps (PbSOMs). The basic motivation behind the introduced technique was to combine the self-organizing quality of the self-organizing maps and generative power of Gaussian mixture models. Experimental results show that the introduced modeling technique using probabilistic self-organizing maps significantly outperforms the traditional technique using the classical GMMs and the EM algorithm or its deterministic variant. More precisely, a relative accuracy improvement of roughly 39% has been gained, as well as, a much less sensitivity to the model-parameters initialization has been exhibited by using the introduced speaker modeling technique using probabilistic self-organizing maps

Yet another algorithm which can generate topography map

Abstract — This paper presents an algorithm to form a topographic map resembling to the self-organizing map. The idea stems on defining an energy function which reveals the local correlation between neighboring neurons. The larger the value of the energy function, the higher the correlation of the neighborhood neurons. On this account, the proposed algorithm is defined as the gradient ascent of this energy function. Simulations on two-dimensional maps are illustrated. Index Terms — Kohonen net, neural network, self-organizing map

Yet Another Algorithm which can Generate Topography Map

This paper presents an algorithm to form a topographic map resembling to the self-organizing map. The idea stems on defining an energy function which reveals the local correlation between neighboring neurons. The larger the value of the energy function, the higher the correlation of the neighborhood neurons. On this account, the proposed algorithm is defined as the gradient ascent of this energy function. Simulations on two-dimensional maps are illustrated