Multi Objective Optimization Problem resolution based on Hybrid Ant-Bee Colony for Text Independent Speaker Verification

Abstract

Today major section of automatic speaker verification (ASV) research is focused on multiple objectives like optimization of feature subset and minimization of Equal Error Rate (EER). As such, numerous systems for feature dimension reduction are proposed. This includes framework coaching and testing analysis for every feature set that could be a time esurient trip. Because of its significance, the issue of feature selection has been researched by numerous scientists. In this paper, a new feature subset selection procedure is presented. Hybrid of Ant Colony and Artificial Bee Colony optimized the feature subset over 85% thereby decreased the computational complexity of ASV. Additionally an external record is maintained to store non-dominated solution vectors for which concept of Pareto dominance is used. An overall optimization of 87% is achieved thereby improved the recognition rate of ASV.