■ For text entry methods to be useful they have to deliver high entry rates and low error rates. At the same time they need to be easy to learn and provide effective means of correcting mistakes. Intelligent text entry methods combine AI techniques with human-computer interaction (HCI) theory to enable users to enter text as efficiently and effortlessly as possible. Here I sample a selection of such techniques from the research literature and set them into their historical context. I then highlight five challenges for text entry methods that aspire to make an impact in our society: localization, error correction, editor support, feedback, and context of use.

We demonstrate Parakeet – a continuous speech recognition system for mobile touch-screen devices. Parakeet’s interface is designed to make correcting errors easy on a handheld device while on the move. Users correct errors using a touch-screen to either select alternative words from a word confusion network or by typing on a predictive software keyboard. Our interface design was guided by computational experiments. We conducted a user study to validate our design. We found novices entered text at 18 WPM while seated indoors and 13 WPM while walking outdoors. Author Keywords Mobile continuous speech recognition, touch-screen interface, error correction, speech input, word confusion network

Correcting recognition errors is often necessary in a speech interface. These errors not only reduce users’ overall entry rate, but can also lead to frustration. While making fewer recognition errors is undoubtedly helpful, facilities for supporting user-guided correction are also critical. We explore how to better support user corrections using Parakeet – a continuous speech recognition system for mobile touch-screen devices. Parakeet’s interface is designed for easy error correction on a handheld device. Users correct errors by selecting alternative words from a word confusion network and by typing on a predictive software keyboard. Our interface design was guided by computational experiments and used a variety of information sources to aid the correction process. In user studies, participants were able to write text effectively despite sometimes high initial recognition error rates. Using Parakeet as an example, we discuss principles we think are important for building effective speech correction interfaces.

Abstract—We investigate how to automatically align spoken corrections with an initial speech recognition result. Such automatic alignment would enable one-step voice-only correction in which users simply respeak their intended text. We present three new models for automatically aligning corrections: a 1-best model, a word confusion network model, and a revision model. The revision model allows users to alter what they intended to write even when the initial recognition was completely correct. We evaluate our models with data gathered from two user studies. We show that providing just a single correct word of context dramatically improves alignment success from 65 % to 84%. We find that a majority of users provide such context without being explicitly instructed to do so. We find that the revision model is superior when users modify words in their initial recognition, improving alignment success from 73 % to 83%. We show how our models can easily incorporate prior information about correction location and we show that such information aids alignment success. Last, we observe that users speak their intended text faster and with fewer re-recordings than if they are forced to speak misrecognized text. I.