VoiceNotes

Stifelman, Lisa; Arons, Barry; Schmandt, Chris; Hulteen, Eric A.

doi:10.1145/169059.169150

Cited by 61 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, annotating digital documents with voice notes can be faster and more convenient than textual notes. Prior research has shown that, when compared to typing using the keyboard, the act of speaking to record notes is faster and allows momentary thoughts to be recorded before they are forgotten [54,67].…”

Section: Voice Note-taking Systemsmentioning

confidence: 99%

Gavin

Khan

Newn

Kelly

et al. 2021

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

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.

show abstract

Section: Voice Note-taking Systemsmentioning

confidence: 99%

Gavin

Khan

Newn

Kelly

et al. 2021

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

show abstract

“…Cord input [32] can provide continuous input through touch location, twisting, bending and pulling but is error-prone and may not always be accessible. Complex interaction can be accomplished with speech recognition [35,30], which offers direct, hands-free user input. But speech-recognition is still suboptimal in noisy environments or in the presence of multiple speakers.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Mid-air List Interaction for Spatial Audio Interfaces

Dicke

Müller

2015

Proceedings of the 3rd ACM Symposium on Spatial User Interaction

View full text Add to dashboard Cite

Selecting items from lists is a common task in many applications. For wearable devices where no display is available, list selection can be challenging. To explore potential solutions we present four user studies evaluating mid-air gestures to interact with lists in an eyes-free interface. We found that a spatialized audio list in the shape of a 110 degree arc angled towards the dominant hand was a comfortable and usable layout for most users. A selection takes less than 10.6 seconds on average and error rates are below 4% when users locate and select an item in an unknown, unordered list of 20 items. For lists of 10 items the mean selection time is 5.5 seconds or less, and error rates drop below 1.4%. We compared monophonic to binaural playback of feedback sounds (musicons) and found no statistical difference for task completion times or error rates between the conditions. We also implemented and evaluated a music player application to showcase spatial audio list selection in an applied scenario.

show abstract