Cooperative Swarms for Clustering Phoneme Data

A new approach for clustering phoneme data is proposed. The proposed approach mimics the behavior of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and, in a same time, in regions far from other places. We tackle phoneme clustering problem using particle swarm optimization(PSO) and utilizing multiple cooperating swarms to obtain cluster centers. The proposed approach is evaluated on phoneme data of TIMIT database. Experimental results show that the proposed clustering approach outperforms single swarm-based clustering as well as k-means, k-harmonic means, and fuzzy c-means clustering approaches.