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Text input on mobile devices without physical keys can be challenging for people who are blind or low-vision. We interview 12 blind adults about their experiences with current mobile text input to provide insights into what sorts of interface improvements may be the most beneficial. We identify three primary themes that were experiences or opinions shared by participants: the poor accuracy of dictation, difficulty entering text in noisy environments, and difficulty correcting errors in entered text. We also discuss an experimental non-visual text input method with each participant to solicit opinions on the method and probe their willingness to learn a novel method. We find that the largest concern was the time required to learn a new technique. We find that the majority of our participants do not use word predictions while typing but instead find it faster to finish typing words manually. Finally, we distill five future directions for non-visual text input: improved dictation, less reliance on or improved audio feedback, improved error correction, reducing the barrier to entry for new methods, and more fluid non-visual word predictions. 

Conversational systems rely heavily on speech recognition to interpret and respond to user commands and queries. Despite progress on speech recognition accuracy, errors may still sometimes occur and can significantly affect the end-user utility of such systems. While visual feedback can help detect errors, it may not always be practical, especially for people who are blind or low-vision. In this study, we investigate ways to improve error detection by manipulating the audio output of the transcribed text based on the recognizer's confidence level in its result. Our findings show that selectively slowing down the audio when the recognizer exhibited uncertainty led to a 12% relative increase in participants' ability to detect errors compared to uniformly slowing the audio. It also reduced the time it took participants to listen to the recognition result and decide if there was an error by 11%. 

We explore a method for presenting word suggestions for non-visual text input using simultaneous voices. We conduct two perceptual studies and investigate the impact of different presentations of voices on a user's ability to detect which voice, if any, spoke their desired word. Our sets of words simulated the word suggestions of a predictive keyboard during real-world text input. We find that when voices are simultaneous, user accuracy decreases significantly with each added word suggestion. However, adding a slight 0.15s delay between the start of each subsequent word allows two simultaneous words to be presented with no significant decrease in accuracy compared to presenting two words sequentially (84% simultaneous versus 86% sequential). This allows two word suggestions to be presented to the user 32% faster than sequential playback without decreasing accuracy. 

In many situations, it may be impractical or impossible to enter text by selecting precise locations on a physical or touchscreen keyboard. We present an ambiguous keyboard with four character groups that has potential applications for eyes-free text entry, as well as text entry using a single switch or a brain-computer interface.We develop a procedure for optimizing these character groupings based on a disambiguation algorithm that leverages a long-span language model. We produce both alphabetically-constrained and unconstrained character groups in an offline optimization experiment and compare them in a longitudinal user study. Our results did not show a significant difference between the constrained and unconstrained character groups after four hours of practice. As expected, participants had significantly more errors with the unconstrained groups in the first session, suggesting a higher barrier to learning the technique.We therefore recommend the alphabetically-constrained character groups, where participants were able to achieve an average entry rate of 12.0 words per minute with a 2.03% character error rate using a single hand and with no visual feedback. 

Text entry is a common and important part of many intelligent user interfaces. However, inferring a user’s intended text from their input can be challenging: motor actions can be imprecise, input sensors can be noisy, and situations or disabilities can hamper a user’s perception of interface feedback. Numerous prior studies have explored input on touchscreen phones, smartwatches, in midair, and on desktop keyboards. Based on these prior studies, we are releasing a large and diverse data set of noisy typing input consisting of thousands of sentences written by hundreds of users on QWERTY-layout keyboards. This paper describes the various subsets contained in this new research dataset as well as the data format. 

Participants in text entry studies usually copy phrases or compose novel messages. A composition task mimics actual user behavior and can allow researchers to better understand how a system might perform in reality. A problem with composition is that participants may gravitate towards writing simple text, that is, text containing only common words. Such simple text is insufficient to explore all factors governing a text entry method, such as its error correction features. We contribute to enhancing composition tasks in two ways. First, we show participants can modulate the difficulty of their compositions based on simple instructions. While it took more time to compose difficult messages, they were longer, had more difficult words, and resulted in more use of error correction features. Second, we compare two methods for obtaining a participant’s intended text, comparing both methods with a previously proposed crowdsourced judging procedure. We found participant-supplied references were more accurate.