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Hands-Free VR

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arxiv 2402.15083 v2 pith:2A733EIH submitted 2024-02-23 cs.HC cs.AIcs.CL

classification cs.HCcs.AIcs.CL
keywords hands-freecommandlanguageparticipantsenglishinterfacerobusttime
verification ladder T0 review T1 audit T2 compute T3 formal
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The paper introduces Hands-Free VR, a voice-based natural-language interface for VR. The user gives a command using their voice, the speech audio data is converted to text using a speech-to-text deep learning model that is fine-tuned for robustness to word phonetic similarity and to spoken English accents, and the text is mapped to an executable VR command using a large language model that is robust to natural language diversity. Hands-Free VR was evaluated in a controlled within-subjects study (N = 22) that asked participants to find specific objects and to place them in various configurations. In the control condition participants used a conventional VR user interface to grab, carry, and position the objects using the handheld controllers. In the experimental condition participants used Hands-Free VR. The results confirm that: (1) Hands-Free VR is robust to spoken English accents, as for 20 of our participants English was not their first language, and to word phonetic similarity, correctly transcribing the voice command 96.71% of the time; (2) Hands-Free VR is robust to natural language diversity, correctly mapping the transcribed command to an executable command in 97.83% of the time; (3) Hands-Free VR had a significant efficiency advantage over the conventional VR interface in terms of task completion time, total viewpoint translation, total view direction rotation, and total left and right hand translations; (4) Hands-Free VR received high user preference ratings in terms of ease of use, intuitiveness, ergonomics, reliability, and desirability.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Survey on Methodological Approaches to Collaborative Embodiment in Virtual Reality

    cs.HC 2025-06 conditional novelty 6.0 of 10

    A PRISMA-based survey of 137 papers maps the methods, metrics, tasks, and gaps in collaborative embodiment VR research.

  2. Can You Move These Over There? An LLM-based VR Mover for Supporting Object Manipulation

    cs.HC 2025-02 conditional novelty 6.0 of 10

    A large language model interprets pointing and speech in VR, and in a 24-person user study this interface significantly speeds up multi-object manipulation, lowers workload and arm fatigue, and improves reported user ...

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