TY - GEN
T1 - Phrase-Gesture Typing on Smartphones
AU - Xu, Zheer
AU - Meng, Yankang
AU - Bi, Xiaojun
AU - Yang, Xing Dong
N1 - Publisher Copyright: © 2022 ACM.
PY - 2022/10/29
Y1 - 2022/10/29
N2 - We study phrase-gesture typing, a gesture typing method that allows users to type short phrases by swiping through all the letters of the words in a phrase using a single, continuous gesture. Unlike word-gesture typing, where text needs to be entered word by word, phrase-gesture typing enters text phrase by phrase. To demonstrate the usability of phrase-gesture typing, we implemented a prototype called PhraseSwipe. Our system is composed of a frontend interface designed specifically for typing through phrases and a backend phrase-level gesture decoder developed based on a transformer-based neural language model. Our decoder was trained using five million phrases of varying lengths of up to five words, chosen randomly from the Yelp Review Dataset. Through a user study with 12 participants, we demonstrate that participants could type using PhraseSwipe at an average speed of 34.5 WPM with a Word Error Rate of 1.1%.
AB - We study phrase-gesture typing, a gesture typing method that allows users to type short phrases by swiping through all the letters of the words in a phrase using a single, continuous gesture. Unlike word-gesture typing, where text needs to be entered word by word, phrase-gesture typing enters text phrase by phrase. To demonstrate the usability of phrase-gesture typing, we implemented a prototype called PhraseSwipe. Our system is composed of a frontend interface designed specifically for typing through phrases and a backend phrase-level gesture decoder developed based on a transformer-based neural language model. Our decoder was trained using five million phrases of varying lengths of up to five words, chosen randomly from the Yelp Review Dataset. Through a user study with 12 participants, we demonstrate that participants could type using PhraseSwipe at an average speed of 34.5 WPM with a Word Error Rate of 1.1%.
KW - gesture input
KW - language model
KW - machine learning
KW - text entry
UR - https://www.scopus.com/pages/publications/85141725282
U2 - 10.1145/3526113.3545683
DO - 10.1145/3526113.3545683
M3 - Conference contribution
T3 - UIST 2022 - Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
BT - UIST 2022 - Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology
PB - Association for Computing Machinery, Inc
T2 - 35th Annual ACM Symposium on User Interface Software and Technology, UIST 2022
Y2 - 29 October 2022 through 2 November 2022
ER -