Smart Interviews Using AI


EOI: 10.11242/viva-tech.01.05.074

Download Full Text here



Citation

Aditi More, Samiksha Mobarkar, Siddhita Salunke, Reshma Chaudhari, "Smart Interviews Using AI ", VIVA-IJRI Volume 1, Issue 5, Article 74, pp. 1-6, 2022. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

With the advent in technology, a lot of our common things have become smart. But our interviewing system still seems to be stuck at same point. If one has low marks it’s fine but a person with a bad personality cannot be hired even if they are satisfying in other aspects, as they do more harm than good. This is the reason online interviews or chatbots are not preferred as many are of the opinion that although every other detail can be thoroughly checked, there is no way they can correctly have a grasp of the interviewee’s personality.Because of COVID-19 pandemic all interviews are taken online but their concerns have increased regarding the aforementioned point. Taking this into consideration, we have built an interview system that analyzes the personality traits of the candidates with the help of facial and speech emotion recognition whose resumes have been approved. For facial emotion recognition we have used CNN model and for speech emotion recognition we have used Google API. .

Keywords

Artificial Intelligence, Convolutional Neural Network, Deep Neural Network, Facial Emotion Recognition, personality, Speech emotion recognition .

References

  1. Y. Sue, H. Suen and K. Hung “Predicting behavioral competencies automatically from facial expressions in real-time video-recorded interviews”, Journal of Real-Time Image Processing, vol.18, 2021, pp. 1011–1021.
  2. I. Stanica, M. Dascula, C. Bodea and A. Moldoveam “VR Job Interview Simulator: Where Virtual Reality Meets Artificial Intelligence For Education”, 2018 Zooming Innovation in Consumer Technologies Conference (ZINC), 2018.
  3. H. Suen, K. Hung and C. Lin “Intelligent video interview agent used to predict communication skill and perceived personality traits”, Human-centric Computing and Information Sciences vol.10, no.03, 2020.
  4. S. Katakwar, O. Mahamuni, N. Inamdar and S. Sadanand “Emotion and Personality Analysis in Recorded Video Interview Using TensorFlow”, International Research Journal of Engineering and Technology (IRJET), vol.08, no.04, 2021.
  5. H. Suen, K. Hung and C. Lin “TensorFlow-based Automatic Personality Recognition Used in Asynchronous Video Interviews”, IEEE Access, vol.07, 2019.
  6. D. Shin, K. Chung and R. Park “Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App”, Applied Sciences, vol.09, no.22, 2019.
  7. E. Frant, I. Ispas, V. Dragomir, M. Dascalu, E. Zoltan and I. Stoica “Voice Based Emotion Recognition with Convolutional Neural Networks”, Romanian Journal Of Information Science And Technology, vol.20, no.03, 2017, pp. 222–240.
  8. M. Salam and I. Idris “Emotion Detection with Hybrid Voice Quality and Prosodic Features using Neural Network”, 2014 4th World Congress on Information and Communication Technologies (WICT 2014), 2014.
  9. R. Burger and M. Dutta “Speech Emotion Recognition with Deep Learning”, 4th International Conference on Signal Processing and Integrated Networks (SPIN), 2017.
  10. H. Hu, M. Xu and W. Wu, “GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition”, Acoustics, Speech and Signal Processing, ICASSP 2007, IEEE International Conference on vol.04, 2007.
  11. T. Liogiene and G. Tamulevicius, "SFS feature selection technique for multistage emotion recognition," 2015 IEEE 3rd Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), 2015, pp. 1-4, doi: 10.1109/AIEEE.2015.7367299.
  12. A. Revathy, P. Shanmugapriya and V. Mohan, "Performance comparison of speaker and emotion recognition," 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), 2015, pp. 1-6, doi: 10.1109/ICSCN.2015.7219844.
  13. R. M. A. H. Manewa and B. Mayurathan, "Emotion Recognition and Discrimination of Facial Expressions using Convolutional Neural Networks.," 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC), 2020, pp. 1-6, doi: 10.1109/R10-HTC49770.2020.9357008.
  14. N. Jaymon, S. Nagdeote, A. Yadav and R. Rodrigues, "Real Time Emotion Detection Using Deep Learning," 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2021, pp. 1-7, doi: 10.1109/ICAECT49130.2021.9392584.
  15. Rangulov, Denis & Fahim, Muhammad “Emotion Recognition on large video dataset based on Convolutional Feature Extractor and Recurrent Neural Network” IEEE 4th International Conference on Image Processing. Applications and Systems (IPAS).