A Review on Existing Systems in Audio & Interview Analysis

A Review on Existing Systems in Audio & Interview Analysis

Vaibhavi Sanap, Kiran Lavhale, Aditya Manchare, Hardik Maind, Mayuri Khade

Computer Engineering Department Sinhgad College of Engineering (SPPU)

Pune, India

Abstract—In the dynamic landscape of recruitment, the need for sophisticated yet equitable interview assessment tools is undeniable. The unpredictability inherent in interviews often leads to apprehension among candidates, potentially impeding their performance. In response, our project introduces a novel Interview Assessment Tool, integrating advanced machine learning techniques with intuitive interfaces to streamline interviews, gauge user confidence, and monitor progress. This tool offers a tailored experience, ensuring data security while upholding the integrity of assessments. By generating job-specific questions, it enhances the relevance of evaluations. The AI-Driven Assessment System provides real-time feedback through natural language processing and audio analysis, facilitating continuous improvement. The Feedback Mechanism delivers comprehensive reports, identifying strengths and areas for growth, fostering self-reflection and skill enhancement. The User Progress Tracking System enables users to monitor their development across multiple interviews. A notable innovation is the incorporation of voice analysis to assess user confidence, providing valuable insights for candidates and recruiters alike. Our Interview Assessment Tool revolutionizes interviews through AI and data-driven insights, delivering a secure, personalized, and equitable assessment experience, empowering candidates to bolster their confidence and excel in the competitive job market. Index Terms— Confidence, Audio-Based Evaluation, Mock Interviews, Speech Emotion Recognition, LSTM, MFCCs