TY - GEN
T1 - AdaptiveVoice: Cognitively Adaptive Voice Interface for Driving Assistance
AU - Wen, Shaoyue
AU - Ping, Songming
AU - Wang, Jialin
AU - Liang, Hai Ning
AU - Xu, Xuhai
AU - Yan, Yukang
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s)
PY - 2024/5/11
Y1 - 2024/5/11
N2 - Current voice assistants present messages in a predefined format without considering users' mental states. This paper presents an optimization-based approach to alleviate this issue which adjusts the level of details and speech speed of the voice messages according to the estimated cognitive load of the user. In the first user study (N = 12), we investigated the impact of cognitive load on user performance. The findings reveal significant differences in preferred message formats across five cognitive load levels, substantiating the need for voice message adaptation. We then implemented AdaptiveVoice, an algorithm based on combinatorial optimization to generate adaptive voice messages in real time. In the second user study (N = 30) conducted in a VR-simulated driving environment, we compare AdaptiveVoice with a fixed format baseline, with and without visual guidance on the Heads-up display (HUD). Results indicate that users benefit from AdaptiveVoice with reduced response time and improved driving performance, particularly when it is augmented with HUD.
AB - Current voice assistants present messages in a predefined format without considering users' mental states. This paper presents an optimization-based approach to alleviate this issue which adjusts the level of details and speech speed of the voice messages according to the estimated cognitive load of the user. In the first user study (N = 12), we investigated the impact of cognitive load on user performance. The findings reveal significant differences in preferred message formats across five cognitive load levels, substantiating the need for voice message adaptation. We then implemented AdaptiveVoice, an algorithm based on combinatorial optimization to generate adaptive voice messages in real time. In the second user study (N = 30) conducted in a VR-simulated driving environment, we compare AdaptiveVoice with a fixed format baseline, with and without visual guidance on the Heads-up display (HUD). Results indicate that users benefit from AdaptiveVoice with reduced response time and improved driving performance, particularly when it is augmented with HUD.
KW - adaptive user interface
KW - driving assistance
KW - Voice interface
KW - workload
UR - https://www.scopus.com/pages/publications/85194829739
U2 - 10.1145/3613904.3642876
DO - 10.1145/3613904.3642876
M3 - Conference Proceeding
AN - SCOPUS:85194829739
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
PB - Association for Computing Machinery
T2 - 2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024
Y2 - 11 May 2024 through 16 May 2024
ER -