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Sabin Bhattarai

dblp:264/7046 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 91% Wearable and physiological sensing · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › text entry
error correction
0.412020
Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze · CHI 2020
Interaction techniques and input › text entry
speech-based text entry
0.412020
Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze · CHI 2020
Interaction techniques and input
text entry
0.412020
Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze · CHI 2020
Wearable and physiological sensing › eye tracking
gaze-based interaction
0.112020
Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze · CHI 2020

Methods — techniques the papers use, named apart from their topics

voice input · 0.4eye tracking · 0.4
YearPublicationVenuePosition
2020 Leveraging Error Correction in Voice-based Text Entry by Talk-and-Gaze
abstract
We present the design and evaluation of Talk-and-Gaze (TaG), a method for selecting and correcting errors with voice and gaze. TaG uses eye gaze to overcome the inability of voice-only systems to provide spatial information. The user's point of gaze is used to select an erroneous word either by dwelling on the word for 800 ms (D-TaG) or by uttering a "select" voice command (V-TaG). A user study with 12 participants compared D-TaG, V-TaG, and a voice-only method for selecting and correcting words. Corrections were performed more than 20% faster with D-TaG compared to the V-TaG or voice-only methods. As well, D-TaG was observed to require 24% less selection effort than V-TaG and 11% less selection effort than voice-only error correction. D-TaG was well received in a subjective assessment with 66% of users choosing it as their preferred choice for error correction in voice-based text entry.
Korok Sengupta, Sabin Bhattarai, Sayan Sarcar, I. Scott MacKenzie, Steffen Staab
CHI2