Speaker Classification with Support Vector Machine and Crossover-Based Particle Swarm Optimization

It has been observed from the literature that speech is the most natural means of communication between humans. Human beings start speaking without any tool or any explicit education. The environment surrounding them helps them to learn the art of speaking. From the existing literature, it is found that the existing speaker classification techniques suffer from over-fitting and parameter tuning issues. An efficient tuning of machine learning techniques can improve the classification accuracy of speaker classification. To overcome this issue, in this paper, an efficient particle swarm optimization-based support vector machine is proposed. The proposed and the competitive speaker classification techniques are tested on the speaker classification data of Punjabi persons. The comparative analysis of the proposed technique reveals that it outperforms existing techniques in terms of accuracy, F-measure, specificity and sensitivity.