Probabilistic Self-Organizing Maps for Text-Independent Speaker Identification

Ayoub Bouziane (Sidi Mohamed Ben Abdellah University)
Jamal Kharroubi (Sidi Mohamed Ben Abdellah University)
Arsalane Zarghili (Sidi Mohamed Ben Abdellah University)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 16, No 1: February 2018

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.