Speaker verification with optimized feature subset using MOBA

In speech processing for speaker verification, feature subset selection is one of the key components. Feature Subset Selection (FS) also played a vital role in the fields like pattern recognition, image processing, data mining, and gene selection. In a real world problem related to speech domain, speech sample contains a large number of relevant and irrelevant features. To increase the speaker verification rate, one needs to use the optimization technique for feature selection after the feature extraction technique. The ultimate goal is to select the most relevant subset of features for error free optimized classification in the speech domain. In this regard a novel feature subset selection algorithm is proposed using Bat algorithm and Multi Objective Optimization technique. Results of the experiment shows the proposed algorithm surpassed the accuracy rates shown by the conventional systems.