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Published:
May 14, 2022DOI:
10.4314/njt.v40i6.10Keywords:
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Main Article Content
In detecting deception using Decision Tree and SVM across different cues
Abstract
Past and present researches in deception detection make use of questioning to ascertain the truth and deceit in the response of the interviewee. In such questioning method, either the verbal or nonverbal cues are closely monitored and analysed to arrive at a decision. Since no single verbal or nonverbal cue is able to reliably detect deception the research proposes to use both the verbal and nonverbal cues to detect deception. Therefore, this research aims to develop a Support Vector Machine and Decision Tree Model to classify the extracted verbal, nonverbal and VerbNon features as deceptive or truthful. The verbal cues capture the speech of the suspect while the nonverbal cues capture the facial expressions of the suspect. The Praat (a tool for speech analysis) was used in extracting all the verbal cues while the nonverbal features were extracted using the Active Shape Model (ASM). The work was implemented in 2015a MatLab. The analysis of the result shows that Decision Tree performs better than SVM in the classification with a percentage score of 93.5% for Nonverbal cues as against that of SVM having percentage score of 91.9%. For verbal and VerbNon cues, Decision Tree recorded 89.9% and 97.6% accuracy while SVM recorded 89.2% and 97.1% accuracy.
