An efficient error correction interface for speech recognition on mobile touchscreen devices

Correcting speech recognition errors on a mobile touchscreen device is an unavoidable but time-consuming task that requires a lot of user effort. To reduce this user effort, we previously proposed an error correction method using long context match with Web N-gram, which we combined with a simple gesture-based user interface. This method automatically replaces an error word with its corresponding correct word. However, it was evaluated only substitution errors in sentences, each of which involves only one error. In this paper, we extend this method to be used for more general cases when a sentence has more than one error. It recovers not only substitution errors but also deletion errors and insertion errors. For recovering deletion errors, it predicts a deleted word based on the phonemes and the part-of-speech tags of its surrounding words. Our experimental results show that the proposed method recovered the errors more accurately with less user effort than the conventional Word Confusion Network based error correction interface.