No abstract

Entering text on non-desktop computing devices is often done via an onscreen virtual keyboard. Input on such keyboards normally consists of a sequence of noisy tap events that specify some amount of text, most commonly a single word. But is single word-at-a-time entry the best choice? This paper compares user performance and recognition accuracy of wordat-a-time, phrase-at-a-time, and sentence-at-a-time text entry on a smartwatch keyboard. We evaluate the impact of differing amounts of input in both text copy and free composition tasks. We found providing input of an entire sentence significantly improved entry rates from 26 wpm to 32 wpm while keeping character error rates below 4%. In offline experiments with more processing power and memory, sentence input was recognized with a much lower 2.0% error rate. Our findings suggest virtual keyboards can enhance performance by encouraging users to provide more input per recognition event.

Making good letter or word predictions can help accelerate the communication of users of high-tech AAC devices. This is particularly important for real-time person-to-person conversations. We investigate whether performing speech recognition on the speakingside of a conversation can improve language model based predictions. We compare the accuracy of three plausible microphone deployment options and the accuracy of two commercial speech recognition engines (Google and IBM Watson). We found that despite recognition word error rates of 7-16%, our ensemble of N-gram and recurrent neural network language models made predictions nearly as good as when they used the reference transcripts.