Abstract
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.
Keywords
Computer science
Feature (linguistics)
Feature selection
Spoken language
Artificial intelligence
Support vector machine
Face (sociological concept)
Naive Bayes classifier
Feature vector
Feature extraction
Natural language processing
Speech recognition
Pattern recognition (psychology)
Linguistics
Subject Areas
Authorship Attribution and Profiling
·Artificial Intelligence ·Physical Sciences
Names, Identity, and Discrimination Research
·Sociology and Political Science ·Social Sciences
Speech Recognition and Synthesis
·Artificial Intelligence ·Physical Sciences
Citations by Year
2022: 1
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