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Abstract

Lie detection has gained importance and is now extremely significant in a variety of fields. It plays an important role in several domains, including law enforcement, criminal investigations, national security,workplace ethics, and personal relationships. As advances in lie detection continue to develop, real-time approaches such asvoice stress technology have emerged as a feasible alternative to traditional methods such as polygraph testing. Polygraph testing, ahistorical and generally established approach, may be enhanced or replaced bythese revolutionary real-time techniques. Traditional lie detection procedures, such aspolygraph testing, have been challenged for their lack of reliability and validity. Newertechniques, such as brain imaging and machine learning, mightofferbetteroutcomes, although they are still in their early phasesand require additional testing.Thisproject intends to explorea deception-detection module based on sophisticated speech-stress analysis techniques that mightbe applied in a real-time deception system. The purpose is to study stress and other articulation cues in voice patterns,to establish their precision and reliability in detecting deceit, by building upon previous knowledge and applying state-of-the-art architecture. The performance and accuracy ofthe system and its audio aspects will be thoroughly analyzed. The ultimate purpose is to contribute to the advancement of more accurate and reliable lie-detection systems,by addressing the limitationsof old techniques and proposing practical solutions for varied applications. This paper proposes an efficient feature-selection strategy,whichusesrandom forest (RF) to selectonly the significant features for trainingwhena real-life trial dataset consisting of audio files isemployed. Next, utilizing the RF as a classifier, an accuracyof 88% is reachedthrough comprehensive evaluation, thereby confirming its reliability and precision for lie-detectionin real-time scenarios.

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