Zheer Xu, Yankang Meng, Xiaojun Bi, Xing-Dong Yang
{"title":"智能手机上的短语手势输入","authors":"Zheer Xu, Yankang Meng, Xiaojun Bi, Xing-Dong Yang","doi":"10.1145/3526113.3545683","DOIUrl":null,"url":null,"abstract":"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%.","PeriodicalId":200048,"journal":{"name":"Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Phrase-Gesture Typing on Smartphones\",\"authors\":\"Zheer Xu, Yankang Meng, Xiaojun Bi, Xing-Dong Yang\",\"doi\":\"10.1145/3526113.3545683\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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%.\",\"PeriodicalId\":200048,\"journal\":{\"name\":\"Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3526113.3545683\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3526113.3545683","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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%.