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Raju*1, G. Swamy*2, J. Rajashekar*3, Dr. V.V. Appaji*4  \n*1,2,3Department of Computer Science and Engineering (AI &ML) Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad, India.  \n*4Guide (Professor), Department of Computer Science and Engineering (AI &ML) Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad, India.  \nABSTRACT  \nThis project presents a self-contained Interview Reviewer, a full-stack application designed to automate the process of technical interview evaluation. The system consists of a Python Flask backend and a React frontend. The frontend allows users to select questions from a pre-defined list, record their audio responses, and submit them for analysis. The core of the application lies in its backend pipeline. It utilizes the wispher model for accurate, on-premise speech-to-text transcription of the candidate’s audio answers. The transcribed text is then processed by a custom Natural Language Processing (NLP) evaluation engine. This engine compares the candidate’s response against predefined keyword and ideal answers, providing a quantitative score and detailed textual feedback based on the correctness and completeness of the answer.  \nKeywords: Interview Reviewer, Full-Stack Application, Technical Interview Evaluation, Python Flask Backend, React Frontend, Audio Response Recording, Whisper Model, Speech-To-Text Transcription, Natural Language Processing Engine, Response Evaluation, Predefined Questions, Keyword Matching, Ideal Answers Comparison, Quantitative Scoring, Textual Feedback, On-Premise Processing.  \nI. INTRODUCTION  \nIn today’s competitive job market, interviews play a very important role in the recruitment process. Companies use interviews to evaluate a candidate’s technical knowledge, communication skills, and problem-solving ability. However, conducting interviews manually requires a lot of time and effort from interviewers. In many cases, interview evaluation may also vary depending on the interviewer’s judgment, which can lead to inconsistency in candidate assessment.  \nWith the rapid development of Artificial Intelligence (AI) and Natural Language Processing (NLP), many intelligent systems are being developed to automate different tasks. Speech recognition technology allows computers to convert spoken language into text, while NLP helps computers understand and analyze human language. These technologies are widely used in voice assistants, chatbots, and automated customer service systems.  \nIn the recruitment field, there is a growing need for auto-mated systems that can help evaluate candidate responses more efficiently. Traditional online assessment platforms mainly focus on multiple-choice questions or coding tests. However, they do not effectively evaluate spoken responses or communication ability, which are important factors during interviews.  \nThe proposed system, Smart Interview Evaluator using Speech-Driven NLP, aims to address this problem by providing an automated interview evaluation platform. In this system, candidates can answer interview questions using voice input through a microphone. The system records the spoken response and converts it into text using a speech-to-text model. After converting the speech into text, NLP techniques are used to analyze the response and compare it with predefined answers.  \nBased on the analysis, the system generates a score and provides feedback to the candidate. This approach helps reduce manual effort, ensures consistent evaluation, and provides quick results. The proposed system can be useful for organizations to conduct preliminary interview assessments and also helps candidates practice and improve their interview skills.  \nII. RELATED RESEARCH  \nIn recent years, many online platforms have been developed to support the recruitment process through automated assessments. These platforms mainly focus on evaluating candidates through coding te","cbCaiiYzmyGmkZbK","https://ap.wps.com/l/cbCaiiYzmyGmkZbK","pdf",591225,"English","# Abstract\n# Introduction\n# Related Research","[{\"question\":\"What is the Smart Interview Evaluator system designed to do?\",\"answer\":\"It automates technical interview evaluation by recording candidates’ spoken answers, transcribing them, and generating scores with textual feedback based on predefined ideal responses.\"},{\"question\":\"How does the system convert audio responses into text?\",\"answer\":\"The backend pipeline uses the Whisper model for on-premise speech-to-text transcription of the candidate’s audio.\"},{\"question\":\"How are candidate answers evaluated after transcription?\",\"answer\":\"A custom NLP evaluation engine compares the transcribed response with predefined keyword and ideal answers, then outputs a quantitative score and detailed feedback reflecting correctness and completeness.\"}]","SMART INTERVIEW EVALUATOR USING SPEECH-DRIVEN NLP | PDF",1790359272,15]