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Voices Unveiled: Quality of Experience in Collaborative VR via AssemblyAI and NASA-TLX Analysis

Authors: Moharana, Bhagyabati; Keighrey, Conor; Murray, Niall;

Voices Unveiled: Quality of Experience in Collaborative VR via AssemblyAI and NASA-TLX Analysis

Abstract

In order to understand the sentiments between users as they worked together as part of a collaborative Virtual Reality (VR) task, this research analyzed voice interaction using Assembly AI. The real-time conversations between 11 pairs of users were captured as they performed a joint puzzle task. The users were in separate physical spaces but connected in a shared virtual setting. The extracted voice sentiments were correlated with self-reported frustration levels and task completion times. The findings reveal a significant positive correlation between negative sentiments and frustration (r=0.73, p≤0.05) and a negative correlation between positive sentiments and task completion time (r=-0.87, p≤0.001). This shows the relationship between sentiment (positive and negative) and task completion in collaborative VR. These results highlight the potential benefits of analyzing voice sentiment in collaborative VR applications.

Keywords

HMD, Sentiment Analysis, Head mounted displays, QoE, Virtual reality, Quality of Experience

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green