Statistical methods for speech recognition
Statistical methods for speech recognition
The design and evaluation of a high-performance soft keyboard
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
The metropolis keyboard - an exploration of quantitative techniques for virtual keyboard design
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
On-line personalization of a touch screen based keyboard
Proceedings of the 8th international conference on Intelligent user interfaces
Phrase sets for evaluating text entry techniques
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Language modeling for soft keyboards
Eighteenth national conference on Artificial intelligence
Tactile feedback for mobile interactions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Text Entry Systems: Mobility, Accessibility, Universality
Text Entry Systems: Mobility, Accessibility, Universality
Eye typing using word and letter prediction and a fixation algorithm
Proceedings of the 2008 symposium on Eye tracking research & applications
Investigating the effectiveness of tactile feedback for mobile touchscreens
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Typing on flat glass: examining ten-finger expert typing patterns on touch surfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Text text revolution: a game that improves text entry on mobile touchscreen keyboards
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Towards online adaptation and personalization of key-target resizing for mobile devices
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Voice typing: a new speech interaction model for dictation on touchscreen devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personalized input: improving ten-finger touchscreen typing through automatic adaptation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Touch behavior with different postures on soft smartphone keyboards
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
Elderly text-entry performance on touchscreens
Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility
Towards the keyboard of oz: learning individual soft-keyboard models from raw optical sensor data
Proceedings of the 2012 ACM international conference on Interactive tabletops and surfaces
Octopus: evaluating touchscreen keyboard correction and recognition algorithms via
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
iGrasp: grasp-based adaptive keyboard for mobile devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Effects of hand drift while typing on touchscreens
Proceedings of Graphics Interface 2013
Proceedings of the 19th international conference on Intelligent User Interfaces
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Soft keyboards offer touch-capable mobile and tabletop devices many advantages such as multiple language support and room for larger displays. On the other hand, because soft keyboards lack haptic feedback, users often produce more typing errors. In order to make soft keyboards more robust to noisy input, researchers have developed key-target resizing algorithms, where underlying target areas for keys are dynamically resized based on their probabilities. In this paper, we describe how overly aggressive key-target resizing can sometimes prevent users from typing their desired text, violating basic user expectations about keyboard functionality. We propose an anchored key-target method which incorporates usability principles so that soft keyboards can remain robust to errors while respecting usability principles. In an empirical evaluation, we found that using anchored dynamic key-targets significantly reduce keystroke errors as compared to the state-of-the-art.