Effects of Language Modeling and its Personalization on Touchscreen Typing Performance
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), ACM, New York, NY, USA, pp. 649-658
Publication Year
2015
Authors
Andrew Fowler, Kurt Partridge, Ciprian Chelba, Xiaojun Bi, Tom Ouyang, Shumin Zhai
BibTeX
Abstract
Modern smartphones correct typing errors and learn userspecific words (such as
proper names). Both techniques are useful, yet little has been published about
their technical specifics and concrete benefits. One reason is that typing accuracy
is difficult to measure empirically on a large scale. We describe a closed-loop,
smart touch keyboard (STK) evaluation system that we have implemented to solve this
problem. It includes a principled typing simulator for generating human-like noisy
touch input, a simple-yet-effective decoder for reconstructing typed words from
such spatial data, a large web-scale background language model (LM), and a method
for incorporating LM personalization. Using the Enron email corpus as a
personalization test set, we show for the first time at this scale that a combined
spatial/language model reduces word error rate from a pre-model baseline of 38.4%
down to 5.7%, and that LM personalization can improve this further to 4.6%.
