Technical talent training effectiveness: vocal versus visual feedback

Xinbo Zhao; Yi Sun; Jun Yu; Baodan Xu

doi:10.1117/12.3033078

5 July 2024 Technical talent training effectiveness: vocal versus visual feedback

Xinbo Zhao, Yi Sun, Jun Yu, Baodan Xu

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131842D (2024) https://doi.org/10.1117/12.3033078
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

With the rapid advancement of digital technology, such as big data, artificial intelligence, cloud computing, etc., these technologies provide unprecedented possibilities for enterprises. Enterprises realize that in order to establish themselves in a fiercely competitive market, they must leverage digital technology to enhance the efficiency of cultivating skilled talents. The cultivation of employee presentation ability is an important guarantee for improving work efficiency. In this study, an Enterprise Technical Talent Training System, E3T, was developed to improve technical talents’ body and vocal expressions. Using the real-time feedback functions, E3T can detect undesirable states, provide instructions to presenters, and help them achieve the desired states. Among the basic operational skills, the practice support functions were implemented to improve face direction, body direction, and voice volume. By comparing the vocal feedback function with the visual feedback function, the results show that vocal feedback is more effective than visual feedback in improving face direction and voice volume.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinbo Zhao, Yi Sun, Jun Yu, and Baodan Xu "Technical talent training effectiveness: vocal versus visual feedback", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131842D (5 July 2024); https://doi.org/10.1117/12.3033078

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KEYWORDS

Visualization

Displays

Computing systems

Education and training

Facial recognition systems

Sensors

Machine learning

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