TY - GEN
T1 - Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones
AU - Pan, Lihang
AU - Yu, Chun
AU - Li, Jia Hui
AU - Huang, Tian
AU - Bi, Xiaojun
AU - Shi, Yuanchun
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/4/29
Y1 - 2022/4/29
N2 - Using voice commands to automate smartphone tasks (e.g., making a video call) can effectively augment the interactivity of numerous mobile apps. However, creating voice command interfaces requires a tremendous amount of effort in labeling and compiling the graphical user interface (GUI) and the utterance data. In this paper, we propose AutoVCI, a novel approach to automatically generate voice command interface (VCI) from smartphone operation sequences. The generated voice command interface has two distinct features. First, it automatically maps a voice command to GUI operations and fills in parameters accordingly, leveraging the GUI data instead of corpus or hand-written rules. Second, it launches a complementary Q&A dialogue to confirm the intention in case of ambiguity. In addition, the generated voice command interface can learn and evolve from user interactions. It accumulates the history command understanding results to annotate the user's input and improve its semantic understanding ability. We implemented this approach on Android devices and conducted a two-phase user study with 16 and 67 participants in each phase. Experimental results of the study demonstrated the practical feasibility of AutoVCI.
AB - Using voice commands to automate smartphone tasks (e.g., making a video call) can effectively augment the interactivity of numerous mobile apps. However, creating voice command interfaces requires a tremendous amount of effort in labeling and compiling the graphical user interface (GUI) and the utterance data. In this paper, we propose AutoVCI, a novel approach to automatically generate voice command interface (VCI) from smartphone operation sequences. The generated voice command interface has two distinct features. First, it automatically maps a voice command to GUI operations and fills in parameters accordingly, leveraging the GUI data instead of corpus or hand-written rules. Second, it launches a complementary Q&A dialogue to confirm the intention in case of ambiguity. In addition, the generated voice command interface can learn and evolve from user interactions. It accumulates the history command understanding results to annotate the user's input and improve its semantic understanding ability. We implemented this approach on Android devices and conducted a two-phase user study with 16 and 67 participants in each phase. Experimental results of the study demonstrated the practical feasibility of AutoVCI.
KW - generation system
KW - interaction-centered nature language understanding
KW - operation sequence
KW - voice command interface
UR - https://www.scopus.com/pages/publications/85130535314
U2 - 10.1145/3491102.3517459
DO - 10.1145/3491102.3517459
M3 - Conference contribution
AN - SCOPUS:85130535314
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
Y2 - 30 April 2022 through 5 May 2022
ER -