Self-Organizing Mixture Models for Text-Independent Speaker Identification

Over the past several years, The Mel-Frequency Cepstral Coefficients (MFCCs) and Gaussian mixture models (GMMs) using the well-known EM algorithm have become the state-of-the-art approach in text-independent speaker recognition applications. However, in recent few years, Self-Organizing Mixture Models which combines the strengths of Self-Organizing Maps and Mixture Models have been proposed in the literature and yielded better results than the classical GMM training in many applications. In this paper, firstly, the implementation and the comparison of the most popular MFCCs variants are done in order to find the best implementation for our speaker identification system. Then, The Self-Organizing Mixture Models are introduced for speaker modeling in text-independent speaker identification. The performance of the Self-Organizing Mixture Models is assessed and compared with the classical Gaussian mixture models using the EM algorithm.