OpenAdaptxt: an open source enabling technology for high quality text entry

Modern text entry systems, especially for touch screen phones and novel devices, rely on complex underlying technologies such as error correction and word suggestion. Furthermore, for global deployment a vast number of languages have to be supported. Together this has raised the entry bar for new text entry techniques, which makes developing and testing a longer process thus stifling innovation. For example, testing a new feedback mechanism in comparison to a stock keyboard now requires the researchers to support at least slip correction and probably word suggestion. This paper introduces OpenAdaptxt: an open source community driven text input platform to enable development of higher quality text input solutions. It is the first commercial-grade open source enabling technology for modern text entry that supports both multiple platforms and dictionary support for over 50 spoken languages

Predicting and Reducing the Impact of Errors in Character-Based Text Entry

This dissertation focuses on the effect of errors in character-based text entry techniques. The effect of errors is targeted from theoretical, behavioral, and practical standpoints. This document starts with a review of the existing literature. It then presents results of a user study that investigated the effect of different error correction conditions on popular text entry performance metrics. Results showed that the way errors are handled has a significant effect on all frequently used error metrics. The outcomes also provided an understanding of how users notice and correct errors. Building on this, the dissertation then presents a new high-level and method-agnostic model for predicting the cost of error correction with a given text entry technique. Unlike the existing models, it accounts for both human and system factors and is general enough to be used with most character-based techniques. A user study verified the model through measuring the effects of a faulty keyboard on text entry performance. Subsequently, the work then explores the potential user adaptation to a gesture recognizer’s misrecognitions in two user studies. Results revealed that users gradually adapt to misrecognition errors by replacing the erroneous gestures with alternative ones, if available. Also, users adapt to a frequently misrecognized gesture faster if it occurs more frequently than the other error-prone gestures. Finally, this work presents a new hybrid approach to simulate pressure detection on standard touchscreens. The new approach combines the existing touch-point- and time-based methods. Results of two user studies showed that it can simulate pressure detection more reliably for at least two pressure levels: regular (~1 N) and extra (~3 N). Then, a new pressure-based text entry technique is presented that does not require tapping outside the virtual keyboard to reject an incorrect or unwanted prediction. Instead, the technique requires users to apply extra pressure for the tap on the next target key. The performance of the new technique was compared with the conventional technique in a user study. Results showed that for inputting short English phrases with 10% non-dictionary words, the new technique increases entry speed by 9% and decreases error rates by 25%. Also, most users (83%) favor the new technique over the conventional one. Together, the research presented in this dissertation gives more insight into on how errors affect text entry and also presents improved text entry methods

Multidimensional Pareto optimization of touchscreen keyboards for speed, familiarity and improved spell checking

The paper presents a new optimization technique for keyboard layouts based on Pareto front optimization. We used this multifactorial technique to create two new touchscreen phone keyboard layouts based on three design metrics: minimizing finger travel distance in order to maximize text entry speed, a new metric to maximize the quality of spell correction quality by minimizing neighbouring key ambiguity, and maximizing familiarity through a similarity function with the standard Qwerty layout. The paper describes the optimization process and resulting layouts for a standard trapezoid shaped keyboard and a more rectangular layout. Fitts' law modelling shows a predicted 11% improvement in entry speed without taking into account the significantly improved error correction potential and the subsequent effect on speed. In initial user tests typing speed dropped from approx. 21wpm with Qwerty to 13wpm (64%) on first use of our layout but recovered to 18wpm (85%) within four short trial sessions, and was still improving. NASA TLX forms showed no significant difference on load between Qwerty and our new layout use in the fourth session. Together we believe this shows the new layouts are faster and can be quickly adopted by users

Power, Performance, and Perception (P3): Integrating Usability Metrics and Technology Acceptance Determinants to Validate a New Model for Predicting System Usage

Currently, there are two distinct approaches to assist information technology managers in the successful implementation of office automation software. The first approach resides within the field of usability engineering, while the second approach is derived from the discipline of management information systems (MIS). However, neither approach has successfully produced conclusive evidence that explains what characteristics facilitate system use as well as influence user acceptance of the system. This study reports on the validity of a new model, entitled the Power, Performance, Perception (P3) model, that links the constructs of usability engineering to user acceptance. Additionally, speech recognition software (SRS) was used in an experimental setting to validate the P3 model. This research also examined the viability of employing SRS in an Air Force office environment. The results of this study failed to validate the P3 model. However, an alternate model for predicting user acceptance, the Usability Acceptance Model, did emerge from the research which showed that the usability metric of user satisfaction can explain 53% of the variance of user intention to use a new technology. Additionally, the results of this study indicate that users in a typical Air Force office environment would utilize SRS for text processing

An adaptable scan-based text entry for mobile devices: Design, predictive modeling, and empirical evaluation

This paper presents a highly customizable assistive on-screen keyboard for mobile devices, which supports several text entry methods based on row-column and bisection scanning techniques. Text entry can be accomplished using a zone based touch screen interface and/or via hardware keypads, involving configurable input control which can range from single-switch solution up to 5-key design. Apart from the presentation of a novel user interface, the paper contributions are as follows: development of movement models for all scan-based methods involved in text entry solution, computation of related upper-bound text entry speed predictions, and empirical investigation of their validity. In order to assess model predictions, a specific instance of row-column scanning technique was juxtaposed to bisection scanning principle in a user study involving 16 participants. Methods are evaluated against text entry performance, required workload, and general usability attributes. Although theoretical models predicted higher entry speed for bisection scanning, the results obtained from experiment demonstrated the row-column technique as significantly more efficient. This outcome discrepancy is specifically discussed by putting emphasis on factors that affect identified relation.

Defining acceptable interaction for universal access

Many new assistive input systems developed to meet the needs of users with functional impairments fail to make it out of the research laboratory and into regular use by the intended users. This paper examines some of the reasons for this and focuses particular on whether the developers of such systems are focusing on the correct metrics for evaluating the functional attributes of the new input technologies. In particular, the paper focuses on the issue of benchmarking new assistive input systems against a baseline measure of interaction rate that takes allowance of factors such as input success/recognition rate, error rate, correction effort and input time. By addressing each of these measures, a more complete understanding of whether an input system is functionally acceptable can be obtained

Better, Faster, Stronger Sequence Tagging Constituent Parsers

Sequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high error rates around closing brackets of long constituents, (b) large label sets, leading to sparsity, and (c) error propagation arising from greedy decoding. To effectively close brackets, we train a model that learns to switch between tagging schemes. To reduce sparsity, we decompose the label set and use multi-task learning to jointly learn to predict sublabels. Finally, we mitigate issues from greedy decoding through auxiliary losses and sentence-level fine-tuning with policy gradient. Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further. On the SPMRL datasets, we observe even greater improvements across the board, including a new state of the art on Basque, Hebrew, Polish and Swedish.Comment: NAACL 2019 (long papers). Contains corrigendu

Near Eyes-Free Chauffeur Computer Interaction with Chording and Visual Text Mnemonics

Modern cars are equipped with advanced technology requiring cognitively complex operation that is reliant on the user’s visual attention. It is therefore hazardous for drivers to operate such devices while driving. In this paper a user interface interaction style for in-car user interfaces are proposed. Users interact with the in-car computer using three chording keys and chording pattern sequences are derived based on visual mnemonics. Cases are illustrated for an in-car multimedia system, a mobile phone and a GPS-navigation system. Experimental results demonstrate that the technique is easy to learn, efficient to use and require low visual attention