Optimization of feature subset using HABC for automatic speaker verification

TLDR: A novel method for speaker verification is proposed using Hybrid Ant Bee Colony optimization to increase the verification rate and the optimized feature subset was 85% with an average accuracy rate of 95.27%.

Abstract: Automatic Speaker Verification is the authentication of a claimed identity based on characteristics of voice. A speaker verification system compares a person's voice with a speaker model or stored voiceprint captured during enrollment as well as an imposter model of different voices, genders and phone types. The system then assigns a confidence score and then makes a decision whether to let the person proceed, to ask for additional voice samples or to refuse entry. Feature subset selection is one of the most concerned processes in the overall classification process of a particular problem. It was also named as dimensionality reduction, attribute subset selection and variable subset selection. For automatic speaker verification (ASV), feature subset selection is one of the first modules. The objective of the paper is to select a most relevant subset of features for error-free optimized classification in the speech domain. In this paper a novel method for speaker verification is proposed using Hybrid Ant Bee Colony optimization to increase the verification rate. Equal Error Rate (EER) is the standard measure which evaluates the projected procedure. Speaker verification system's accuracy rates surpassed the results of traditional systems after applying proposed optimization algorithm; the optimized feature subset was 85% with an average accuracy rate of 95.27%.