Demographic information classification exploiting spoken language

Recently, extracting the demographic information like age, gender and race by using speech and face attributes takes much attention in the literature. In this research, we have focused on the implementation of a demographic information classification system and proved the relationship between spoken language and demographic profile of people. In the first step, the feature vectors of spoken language were extracted then dimensions of the feature vectors were reduced by our feature reduction method and Correlation Based Feature Selection method. Finally, the success of Naïve Bayes, Support Vector Machine and K-Nearest Neigbour classification algorithms was evaluated.