Ku Bong Min  Jinwook Seo

10.5626/JOK.2022.49.4.321

Smartwatch text entry statistical decoder Corpus Language Model touch model

When a statistical decoder is used to support text entry on a smartwatch, fast and accurate typing is possible. In this paper, we analyzed the effect of a corpus, which is used to construct a language model necessary to implement the autocorrect function, on the accuracy of character input. Language models are based on the Brown corpus, which consists of text of various genres, and the Twitter corpus, extracted from tweet messages. We constructed a statistical decoder for the autocorrect function of the text entry using the two language models, and we simulated user touch input with the dual Gaussian distribution on the smartwatch keyboard to input Enron mobile phrases, composed of phrases written on real mobile devices. The test result shows that the average character error rate (CER) of the Brown corpus and the Twitter corpus is 8.35% and 6.44%, respectively, confirming a statistically significant difference.