Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review

Arushi, Roberto Dillon, Ai Ni Teoh, Denise Dillon
8/1/2022
eess.AScs.HCcs.LG

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Abstract

Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research has been conducted to detect stress during public speaking in real time using voice analysis. In this context, the current review showed that the application of algorithms was not properly explored and helped identify the main obstacles in creating a suitable testing environment while accounting for current complexities and limitations. In this paper, we present our main idea and propose a stress detection computational algorithmic model that could be integrated into a Virtual Reality (VR) application to create an intelligent virtual audience for improving public speaking skills. The developed model, when integrated with VR, will be able to detect excessive stress in real time by analysing voice features correlated to physiological parameters indicative of stress and help users gradually control excessive stress and improve public speaking performance

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Cite this paper

@article{arushi2022voice,
  title  = {Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review},
  author = {Arushi and Roberto Dillon and Ai Ni Teoh and Denise Dillon},
  year   = {2022},
  eprint = {2208.01041},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2208.01041v1}
}

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