Speech Vocal Clustering Methods

Abstract

The problem of increasing the clustering efficiency of vocal speech sounds is considered. Centroid and medoid clustering methods that use normalized distances which increases the efficiency of clustering, are proposed. Characteristics and quality criteria based on them are suggested for these methods. These methods have been investigated on TIMIT database and are intended for intelligent biometric identification systems.