GAVIN:Gaze-Assisted Voice-Based Implicit Note-taking

Khan, A.A. and Newn, J. and Kelly, R.M. and Srivastava, Namrata and Bailey, James and Velloso, Eduardo (2021) GAVIN:Gaze-Assisted Voice-Based Implicit Note-taking. ACM Transactions on Computer-Human Interaction, 28 (4). ISSN 1073-0516

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Annotation is an effective reading strategy people often undertake while interacting with digital text. It involves highlighting pieces of text and making notes about them. Annotating while reading in a desktop environment is considered trivial but, in a mobile setting where people read while hand-holding devices, the task of highlighting and typing notes on a mobile display is challenging. In this article, we introduce GAVIN, a gaze-assisted voice note-taking application, which enables readers to seamlessly take voice notes on digital documents by implicitly anchoring them to text passages. We first conducted a contextual enquiry focusing on participants’ note-taking practices on digital documents. Using these findings, we propose a method which leverages eye-tracking and machine learning techniques to annotate voice notes with reference text passages. To evaluate our approach, we recruited 32 participants performing voice note-taking. Following, we trained a classifier on the data collected to predict text passage where participants made voice notes. Lastly, we employed the classifier to built GAVIN and conducted a user study to demonstrate the feasibility of the system. This research demonstrates the feasibility of using gaze as a resource for implicit anchoring of voice notes, enabling the design of systems that allow users to record voice notes with minimal effort and high accuracy.

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Journal Article
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ACM Transactions on Computer-Human Interaction
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14 Feb 2022 16:25
Last Modified:
30 Sep 2023 00:44