Mining, analyzing, and modeling text written on mobile devices

AbstractWe present a method for mining the web for text entered on mobile devices. Using searching, crawling, and parsing techniques, we locate text that can be reliably identified as originating from 300 mobile devices. This includes 341,000 sentences written on iPhones alone. Our data enables a richer understanding of how users type “in the wild” on their mobile devices. We compare text and error characteristics of different device types, such as touchscreen phones, phones with physical keyboards, and tablet computers. Using our mined data, we train language models and evaluate these models on mobile test data. A mixture model trained on our mined data, Twitter, blog, and forum data predicts mobile text better than baseline models. Using phone and smartwatch typing data from 135 users, we demonstrate our models improve the recognition accuracy and word predictions of a state-of-the-art touchscreen virtual keyboard decoder. Finally, we make our language models and mined dataset available to other researchers.</jats:p

Fast and precise touch-based text entry for head-mounted augmented reality with variable occlusion

We present the VISAR keyboard: An augmented reality (AR) head-mounted display (HMD) system that supports text entry via a virtualised input surface. Users select keys on the virtual keyboard by imitating the process of single-hand typing on a physical touchscreen display. Our system uses a statistical decoder to infer users’ intended text and to provide error-tolerant predictions. There is also a high-precision fall-back mechanism to support users in indicating which keys should be unmodified by the auto-correction process. A unique advantage of leveraging the well-established touch input paradigm is that our system enables text entry with minimal visual clutter on the see-through display, thus preserving the user’s field-of-view. We iteratively designed and evaluated our system and show that the final iteration of the system supports a mean entry rate of 17.75wpm with a mean character error rate less than 1%. This performance represents a 19.6% improvement relative to the state-of-the-art baseline investigated: A gaze-then-gesture text entry technique derived from the system keyboard on the Microsoft HoloLens. Finally, we validate that the system is effective in supporting text entry in a fully mobile usage scenario likely to be encountered in industrial applications of AR HMDs.Per Ola Kristensson was supported in part by a Google Faculty research award and EPSRC grants EP/N010558/1 and EP/N014278/1. Keith Vertanen was supported in part by a Google Faculty research award. John Dudley was supported by the Trimble Fund

Velocitap: Investigating fast mobile text entry using sentence-based decoding of touchscreen keyboard input

We present VelociTap: a state-of-the-art touchscreen keyboard decoder that supports a sentence-based text entry approach. VelociTap enables users to seamlessly choose from three word-delimiter actions: pushing a space key, swiping to the right, or simply omitting the space key and letting the decoder infer spaces automatically. We demonstrate that VelociTap has a significantly lower error rate than Google’s keyboard while retaining the same entry rate. We show that intermediate visual feedback does not significantly affect entry or error rates and we find that using the space key results in the most accurate results. We also demonstrate that enabling flexible word-delimiter options does not incur an error rate penalty. Finally, we investigate how small we can make the keyboard when using VelociTap. We show that novice users can reach a mean entry rate of 41 wpm on a 40mm wide smartwatch-sized keyboard at a 3% character error rate.This is the accepted manuscript. The final version is available from ACM at http://dl.acm.org/citation.cfm?id=2702135

VelociWatch: Designing and evaluating a virtual keyboard for the input of challenging text

© 2019 Association for Computing Machinery. Virtual keyboard typing is typically aided by an auto-correct method that decodes a user’s noisy taps into their intended text. This decoding process can reduce error rates and possibly increase entry rates by allowing users to type faster but less precisely. However, virtual keyboard decoders sometimes make mistakes that change a user’s desired word into another. This is particularly problematic for challenging text such as proper names. We investigate whether users can guess words that are likely to cause auto-correct problems and whether users can adjust their behavior to assist the decoder. We conduct computational experiments to decide what predictions to ofer in a virtual keyboard and design a smartwatch keyboard named VelociWatch. Novice users were able to use the features of VelociWatch to enter challenging text at 17 words-per-minute with a corrected error rate of 3%. Interestingly, they wrote slightly faster and just as accurately on a simpler keyboard with limited correction options. Our fnding suggest users may be able to type dif-fcult words on a smartwatch simply by tapping precisely without the use of auto-correct

A Design Engineering Approach for Quantitatively Exploring Context-Aware Sentence Retrieval for Nonspeaking Individuals with Motor Disabilities

Nonspeaking individuals with motor disabilities typically have very low communication rates. This paper proposes a design engineering approach for quantitatively exploring contextaware sentence retrieval as a promising complementary input interface, working in tandem with a word-prediction keyboard. We motivate the need for complementary design engineering methodology in the design of augmentative and alternative communication and explain how such methods can be used to gain additional design insights. We then study the theoretical performance envelopes of a context-aware sentence retrieval system, identifying potential keystroke savings as a function of the parameters of the subsystems, such as the accuracy of the underlying auto-complete word prediction algorithm and the accuracy of sensed context information under varying assumptions. We find that context-aware sentence retrieval has the potential to provide users with considerable improvements in keystroke savings under reasonable parameter assumptions of the underlying subsystems. This highlights how complementary design engineering methods can reveal additional insights into design for augmentative and alternative communication

Recognition and Correction of Voice Web Search Queries

In this work we investigate how to recognize and correct voice web search queries. We describe our corpus of web search queries and show how it was used to improve recognition accuracy. We show that using a search-specific vocabulary with automatically generated pronunciations is superior to using a vocabulary limited to a fixed pronunciation dictionary. We conducted a formative user study to investigate recognition and correction aspects of voice search in a mobile context. In the user study, we found that despite a word error rate of 48%, users were able to speak and correct search queries in about 18 seconds. Users did this while walking around using a mobile touch-screen device. Copyright © 2009 ISCA

Performance comparisons of phrase sets and presentation styles for text entry evaluations

We empirically compare five different publicly-available phrase sets in two large-scale (N = 225 and N = 150) crowdsourced text entry experiments. We also investigate the impact of asking participants to memorize phrases before writing them versus allowing participants to see the phrase during text entry. We find that asking participants to memorize phrases increases entry rates at the cost of slightly increased error rates. This holds for both a familiar and for an unfamiliar text entry method. We find statistically significant differences between some of the phrase sets in terms of both entry and error rates. Based on our data, we arrive at a set of recommendations for choosing suitable phrase sets for text entry evaluations. Copyright © 2012 ACM

Mining, analyzing, and modeling text written on mobile devices

© Cambridge University Press 2019. We present a method for mining the web for text entered on mobile devices. Using searching, crawling, and parsing techniques, we locate text that can be reliably identified as originating from 300 mobile devices. This includes 341,000 sentences written on iPhones alone. Our data enables a richer understanding of how users type "in the wild" on their mobile devices. We compare text and error characteristics of different device types, such as touchscreen phones, phones with physical keyboards, and tablet computers. Using our mined data, we train language models and evaluate these models on mobile test data. A mixture model trained on our mined data, Twitter, blog, and forum data predicts mobile text better than baseline models. Using phone and smartwatch typing data from 135 users, we demonstrate our models improve the recognition accuracy and word predictions of a state-of-the-art touchscreen virtual keyboard decoder. Finally, we make our language models and mined dataset available to other researchers