Probabilistic Self-Organizing Maps for Text-Independent Speaker Identification

Ayoub Bouziane, Jamal Kharroubi, Arsalane Zarghili


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.


speaker identification system; gaussian mixture model (GMM); probabilistic self-organizing maps; EM algorithm; deterministic annealing EM algorithm; the SOEM algorithm

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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