Abstract: Effective communication is a critical social skill that helps us understand and relate to others. It is also crucial in job-related interviews. In this project, candidates' communication skills in two types of behavioural interviews—a written interview that includes a brief essay and an interface-based, asynchronous video interview—are methodically studied and automatically measured. Next, by utilizing deep learning methods and machine learning XGBOOST, we suggest a prediction model that makes use of automatically extracted multimodel features such as audio, visual, and lexical. While all currently available technologies predict essays with an accuracy of 80–90%, our XGBOOST technology predicts essays with an accuracy of 95–96%. The capacity to effectively and efficiently convey knowledge to others is known as communication proficiency.

Keywords: Communication Skills, XGBOOST, Interviews, Deep Learning, CNN.

Cite:
V. Ratna Sri, T. Anuhya, V. Mounika, P. Amulya,"Automatic Evaluation of Communication Competency in Diverse Environments", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 3, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11340.

| DOI: 10.17148/IARJSET.2024.11340