Voice assistants (VAs) like Siri and Alexa are transforming human-computer interaction; however, they lack awareness of users’ spatiotemporal context, resulting in limited performance and unnatural dialogue. We introduce GazePointAR, a fully-functional context-aware VA for wearable augmented reality that leverages eye gaze, pointing gestures, and conversation history to disambiguate speech queries. With GazePointAR, users can ask “what’s over there?” or “how do I solve this math problem?” simply by looking and/or pointing. We evaluated GazePointAR in a three-part lab study (N=12): (1) comparing GazePointAR to two commercial systems, (2) examining GazePointAR’s pronoun disambiguation across three tasks; (3) and an open-ended phase where participants could suggest and try their own context-sensitive queries. Participants appreciated the naturalness and human-like nature of pronoun-driven queries, although sometimes pronoun use was counter-intuitive. We then iterated on GazePointAR and conducted a first-person diary study examining how GazePointAR performs in-the-wild. We conclude by enumerating limitations and design considerations for future context-aware VAs.
Citation:
Jaewook Lee, Jun Wang, Elizabeth Brown, Liam Chu, Sebastian S. Rodriguez, and Jon E. Froehlich. 2024. GazePointAR: A Context-Aware Multimodal Voice Assistant for Pronoun Disambiguation in Wearable Augmented Reality. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24). Association for Computing Machinery, New York, NY, USA, Article 408, 1–20. [ Ссылка ]
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