Assessment and prediction of communication competency of the students for placement readiness using speech signal processing and kernels of support vector classifiers

This research work is carried out to assess and predict the classification on communication competency among students using different kernels of support vector classifiers (SVC). The communication competency assessment and prediction classification model eliminates the human intervention in judging the communication competency among student for the purpose of placement. The model uses pre-recorded speech signal and the required features are drawn and analyzed in speech signal processing software. The prediction model classifies the competency as Good, Average and Poor which helps in faster assessment of student’s communication competency efficiently, saving lot of time involved in scheduling and conducting presentation activity for trainers The prediction model is constructed using support vector classifier kernels. All the kernels are applied and the result is judged and analyzed for its suitability to get more accuracy in competency prediction. The model predicted with average accuracy rate of 94%.