Pages: 38-43
Shalini Bhaskar Bajaj
As a steppingstone towards a candidate's career, a job interview is perhaps the most important occasion in which their distinct set of skills meets an expected opportunity. They are perhaps one of the most essential elements in educational system and corporate world, which when used properly can yield competent candidates to hire, and are based on testing of their skills. Mock interviews enhance communication skills, as well as confidence, which translates into improved interview performance. We present a new simulation system for practicing interviews, powered by artificial intelligence, designed to narrow the preparation-performance gap. Our system measures performance in two critical areas: empathy and confidence. For evaluating emotions, we apply sophisticated approaches to deep learning, particularly classifying facial expressions into seven basic emotions using a convolutional neural network (CNN). In analyzing confidence, we employ voice recognition and natural language processing (NLP) and LSTM (Long Short-Term Memory) models to process and identify speech correctly. The system helps attendees overcome stressful pre-interview situations, improves their self-perception and self-efficacy, and prepares them for real-life interviews.
Artificial Intelligence, Deep Learning, Convolutional Neural Network (CNN), Voice Recognition, Natural Language Processing (NLP)