Probabilistic Self-Organizing Maps for Text-Independent Speaker Identification

Abstract

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.

Towards an objective comparison of feature extraction techniques for automatic speaker recognition systems

“…The feature extraction component involves the processing of speech signal and the extraction of speaker-specific and discriminative characteristics as shown in Figure 1. The modeling & scoring block aims to train a reference model for each client speaker on the basis of its extracted features, as well as, to score the test utterances [1,2].…”