Bio-inspired Approach for Automatic Speaker Clustering Using Auditory Modeling and Self-Organizing Maps

This paper presents a biologically inspired approach to the problem of voice biometrics. The aim of this study is to examine the capacity of the automatic system, based on a physiologically appropriate auditory model and self-organizing neural networks, to distinguish the voices of different speakers. The idea stems from the human ability to successfully extract various information from speech in the process of verbal communication in different acoustic conditions, including recognizing the identity of a familiar person by their voice. Based on the obtained results, one can conclude that the proposed method has demonstrated high-quality unsupervised classification.