A Convolutional Neural Network Approach To Interview Simulation And Evaluation

Abstract

Artificial Intelligence (AI) Is Transforming A Wide Range Of Industries By Enabling Machines To Perform Tasks That Traditionally Required Human Intelligence, Such As Perception, Decision-making, And Natural Interaction. In The Context Of Recruitment, Traditional Interview Methods Often Focus Primarily On Technical Skills, Neglecting Important Aspects Like Emotional Intelligence And Candidate Confidence. This Paper Presents An AI-powered Mock Interview Evaluator That Offers A More Holistic Assessment By Analyzing Emotional Expressions And Confidence Levels In Real Time. The System Combines Convolutional Neural Networks (CNNs) For Facial Emotion Recognition And Recurrent Neural Networks (RNNs) For Analyzing Speech And Body Language. Trained On A Diverse Dataset Of Mock Interviews, The Model Can Detect Emotions Such As Happiness, Sadness, Anger, And Surprise, While Also Estimating Confidence Through Multimodal Analysis.

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