Text Entry by Inference: Eye Typing, Stenography, and Understanding Context of Use

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My research is based on the observation that our daily interaction with computers is highly redundant. Some of these redundancies can be modelled and exploited by intelligent user interfaces. Intelligent text entry methods use AI techniques such as machine learning to exploit redundancies in our languages. They enable users to write quickly and accurately, without the need for a key press for every single intended letter.In this programme I propose to develop two new intelligent text entry methods. The first is a system that enables disabled users to communicate efficiently using an eye-tracker. The second system is a novel intelligent text entry method that is inspired by stenography.In addition, I propose to explore text entry methods' broader context. The research literature has concentrated on inventing text entry methods that promise high entry rates and low error rates. Now that we have text entry methods that have reasonably high entry rates it is time to complement this objective function by discovering other aspects of text entry. I propose to use social-science techniques, such as diary and field-studies, to understand how users would prefer to use text entry methods in the wild. System 1: Eye-typing by inferenceThis is a system that will potentially increase the entry rate in eye-typing systems. Current eye-typing systems are inherently slow (due to the dwell timeouts), and users perceive them as frustrating. I propose to build a system that enables users to eye-type without the need for a dwell timeout at all. Potentially, my method will be faster than any other eye-tracker based method in the world.With my proposed system users write words by directing their gaze at the intended letter keys, in sequence. Users' intended words are transcribed when they look at a result area positioned above the keyboard. Users can write more than one word. They can also write sequences or words, or even stop short within a word. They may go to the spacebar key between words but this is not strictly necessary for the system to be able to correctly infer users' intended words.System 2: Stenography by inferenceThis system will be a stenography system for pen or single-finger input. The primary application is mobile text entry. However, I strive to create a system that to some extent can replace the desktop keyboard, should users so desire. Potentially it will be faster than any other pen-based text entry method.The idea behind this method is to enable users to write words quickly by gesturing patterns they have previously learned. Such open-loop recall from muscle-memory is much faster than the closed-loop visually-guided motions users are required to perform when they tap on, for example, an on-screen keyboard. My proposed system will enable users to quickly and accurately articulate gestures for individual words. These gestures will be fixed for a particular word. That is, each word is associated with a single (prototypical) unique gestural pattern. A user's input gesture is recognised by a pattern recognizer. The word whose closest pattern best match the user's input gesture will be outputted by the system as the user's intended word.Understanding the broader context of text entryThe last component of my proposed programme serves to contribute new perspectives to the text entry research field. As previously discussed, context of use is largely unexplored in text entry. I intend to explore this topic using a range of qualitative methods. I intend to perform interviews, conduct field studies (e.g. studying participants trying a prototype mobile speech recognizer at a caf), and diary-studies. The latter will be conducted with a system that provides users of a choice of a few text entry methods that I hypothesize will be useful for different situations. I also intend to read literature on design and architecture to further my understanding of the complete design space of text entry.