Hierarchical Speech Recognition System Using MFCC Feature Extraction and Dynamic Spiking RSOM

In this paper, we propose new variants of unsupervised and competitive learning algorithms designed to deal with temporal sequences. These algorithms combine features from Spiking Neural Networks (SNNs) and the advantages of the hierarchical self organizing map (HSOM). The first variant named Hierarchical Dynamic recurrent spiking self-organizing map (HD-RSSOM) is characterized by the integration of a temporal controller component to regulate the firing activity of the spiking neurons. The second variant is a hierarchical model which represents a multi-layer extension of HD-RSSOM model. The case study of the proposed HSOM variants is phonemes and words recognition in continuous speech. The applied HSOM variants serve as tools for developing intelligent systems and pursuing artificial intelligence applications.