Text-independent speaker identification using neuralnetsand AR-vector models

Abstract

A method for speaker identification based on the analysis of four sets of speech parameters is proposed. The recognition is carried out by means of two classifiers: the first is based on self-organising Kohonen maps, a new prototype distribution map and a new similarity measure, and the second on AR-vector models. The final decision is realised by a voting principle using the classifiers' decisions.