EOI: 10.11242/viva-tech.01.04.104

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Mr. Jay Patel, Ms. Disha Sakre, Mr. Dheeraj Purohit, Prof. Dnyaneshwar Bhabad , "NLP BASED INTERVIEW ASSESSMENT SYSTEM", VIVA-IJRI Volume 1, Issue 4, Article 104, pp. 1-6, 2021. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


Online interview is not a new thing but in this covid-19 situation it seems to be the only option. However, assessing the candidate on a video call may not be that effective. Having an AI based Interview Assessment System could prove to be useful, which would take input as speech and will give output as detailed analysis of that speech. While most the research work currently done focuses only on finding sentiment or personality from speech, our system aims to extract multiple information from the speech and provide a detailed analysis. The analysis would include a detailed report containing results about confidence level of the person, his/her emotional state, speed of the speech, frequently repeated words and also personality reflected by that speech. An interview panel consists of various members focusing on different aspect of the answer given by the candidate, some focus on technical correctness while, some simply want to check the communication skills of the candidate. Having an AI system giving a report on the soft skills part would reduce the work for interviewer and he/she could give complete focus on the technical correctness of the answer. This could eventually help save time and resources used by organizations for hiring process. This intention of creating this system is to assist the interview process and give analysis report based on the speech input instead a giving a verdict about selection of the candidate. Thus, this system could use not only by the interviewers but also by the candidates. The output provided would be a detailed report which could prove to be a good feedback for the students who are preparing for the interview. Having a feedback would help candidates work on their week points and thus perform better in further interviews.


natural language processing, neural network, personality detection, regression model, speech signal.


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