Hierarchical Evolutionary Learning of Growing Incremental Self-Organizing Maps for Phoneme Classification

The aim of this work is to design a hierarchical model which represents a multi-layer extension of Self-Organizing Map (SOM) variant. The purpose of the proposed system is to create autonomous systems that can learn independently and cooperate to provide a better decision of the phoneme classification. The basic SOM variant is a hybrid model of SOM and Genetic Algorithm (GA) using a growing incremental technique to adapt the map structure and extra information in the map units to optimise the map codebooks. The hierarchical evolutionary learning algorithm classifies data according to tow hierarchical levels ensuring the representation of the hierarchical relations of the data. Our experiments yielded a high recognition rate of 77.73% on TIMIT acoustic-phonetic continuous speech corpus.