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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65655

AI Virtual Interview Trainer with Real-Time Feedback Using Natural Language Processing, Speech Emotion Recognition, and Computer Vision

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65655

Volume:

10 |

6 |

Jun 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65655

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Yukesh Kumar R

Yukesh Kumar R

Department of Computer Application

Nehru Institute of Information Technology and Management

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Sivakumar P

Sivakumar P

Department of Computer Application

Nehru Institute of Information Technology and Management

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Abstract

This project presents an AI-powered virtual interview platform that evaluates candidates in real time using Natural Language Processing (NLP), Computer Vision, and Speech Emotion Recognition. The system conducts HR and technical interviews, analyzes spoken responses, facial expressions, eye contact, posture, confidence, and communication skills, and generates personalized feedback reports. The proposed framework aims to improve interview readiness while reducing dependence on human interviewers.

KEYWORDS

Artificial Intelligence Virtual Interview NLP Computer Vision Speech Emotion Recognition Deep Learning Interview Analytics.

Cite This Article

Yukesh Kumar R, Sivakumar P, "AI Virtual Interview Trainer with Real-Time Feedback Using Natural Language Processing, Speech Emotion Recognition, and Computer Vision," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-3, Jun 2026. https://doi.org/10.55041/IJSREM65655

Full Article/Conclusion

The proposed AI Virtual Interview Trainer combines NLP, Speech Emotion Recognition, and Computer Vision into a unified intelligent platform. It enables realistic interview practice with detailed analytics and personalized recommendations, improving candidates' confidence and employability.

References

[1]       Devlin et al., BERT, NAACL, 2019. [2]       Radford et al., Whisper, OpenAI, 2022. [3]       Howard et al., MediaPipe, 2020. [4]       Serengil & Ozpinar, DeepFace, 2021. [5]       Reimers & Gurevych, Sentence-BERT, EMNLP, 2019. [6]       Russell & Norvig, Artificial Intelligence: A Modern Approach. [7]       Goodfellow et al., Deep Learning, MIT Press.

Editorial & Review Transparency

Publication Date

6/29/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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8

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

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